The China toll deepens: Growth in the bilateral trade deficit between 2001 and 2017 cost 3.4 million U.S. jobs, with losses in every state and congressional district

Summary and key findings

The United States has a massive trade deficit with China. The growth of the U.S. trade deficit with China, which has increased by more than $100 billion since the beginning of the Great Recession, almost entirely explains why manufacturing employment has not fully recovered along with the rest of the economy. And the growing trade deficit with China isn’t just a post-recession phenomenon hitting manufacturing: it has cost the U.S. millions of jobs throughout the economy since China entered the World Trade Organization (WTO) in 2001, a finding validated by numerous studies.

This report underscores the ongoing trade and jobs crisis by updating EPI’s research series on the jobs impact of the U.S.–China trade deficit. The most recent of these reports (Scott 2012; Kimball and Scott 2014; Scott 2017a) look at the effect of the U.S. trade deficit with China since China entered the WTO in 2001. Our model examines the job impacts of trade by subtracting the job opportunities lost to imports from those gained through exports. As with our previous analyses, we find that because imports from China have soared while exports to China have increased much less, the United States is both losing jobs in manufacturing (in electronics and high tech, apparel, textiles, and a range of heavier durable goods industries) and missing opportunities to add jobs in manufacturing (in exporting industries such as transportation equipment, agricultural products, computer and electronic parts, chemicals, machinery, and food and beverages).

The growing trade deficit with China since China entered the WTO affects different regions in different ways. Some regions are devastated by layoffs and factory closings while others are surviving but not growing the way they could be if new factories were opening and existing plants were hiring more workers. This slowdown in manufacturing job generation is also contributing to stagnating wages of typical workers and widening inequality.

Following are the key highlights of this report:

U.S. jobs lost are spread throughout the country but are concentrated in manufacturing, including in industries in which the United States has traditionally held a competitive advantage.

The growth of the U.S. trade deficit with China between 2001 and 2017 was responsible for the loss of 3.4 million U.S. jobs, including 1.3 million jobs lost since 2008 (the first full year of the Great Recession, which technically began at the end of 2007). Nearly three-fourths (74.4 percent) of the jobs lost between 2001 and 2017 were in manufacturing (2.5 million manufacturing jobs lost).

The growing trade deficit with China has cost jobs in all 50 states and in every congressional district in the United States. The 10 hardest-hit states, when looking at job loss as a share of total state employment, were New Hampshire, Oregon, California, Minnesota, North Carolina, Rhode Island, Massachusetts, Vermont, Wisconsin, and Texas. Job losses in these states ranged from 2.57 percent (in Texas) to 3.55 percent (in New Hampshire) of total state employment. The five hardest-hit states based on total jobs lost were California (562,500 jobs lost), Texas (314,000), New York (183,500), Illinois (148,200), and Pennsylvania (136,100).

The trade deficit in the computer and electronic parts industry grew the most: 1,209,000 jobs were lost in that industry, accounting for 36.0 percent of the 2001–2017 total jobs lost. Not surprisingly, the hardest-hit congressional districts (those ranking in the top 20 districts in terms of jobs lost as a share of all jobs in the district) included districts in Arizona, California, Illinois, Massachusetts, Minnesota, New York, Oregon, and Texas, where jobs in that industry are concentrated. A district in Georgia and another in North Carolina were also especially hard hit by trade-related job displacement in a variety of manufacturing industries, including computer and electronic parts, textiles and apparel, and furniture.

Surging imports of steel, aluminum, and other capital-intensive products threaten hundreds of thousands of jobs in key industries such as primary metals, machinery, and fabricated metal products as well.

Global trade in advanced technology products—often discussed as a source of comparative advantage for the United States—is instead dominated by China. This broad category of high-end technology products includes the more advanced elements of the computer and electronic parts industry as well as other sectors such as biotechnology, life sciences, aerospace, and nuclear technology. In 2017, the United States had a $135.4 billion trade deficit in advanced technology products with China, and this deficit was responsible for 36.1 percent of the total U.S.–China goods trade deficit that year. In contrast, the United States had a $24.5 billion trade surplus in advanced technology products with the rest of the world in 2017.

Growing trade deficits are also associated with wage losses not just for manufacturing workers but for all workers economywide who don’t have a college degree.

Between 2001 and 2011 alone, growing trade deficits with China reduced the incomes of directly impacted workers by $37 billion per year, and in 2011 alone, growing competition with imports from China and other low wage-countries reduced the wages of all U.S. non–college graduates by a total of $180 billion. Most of that income was redistributed to corporations in the form of higher profits and to workers with college degrees at the very top of the income distribution through higher wages.

The U.S. trade deficit with China has increased since China entered into the WTO

U.S. proponents of admitting China into the World Trade Organization frequently claimed that letting China into the WTO would increase U.S. exports, shrink the U.S. trade deficit with China, and create jobs in the United States.1 In 2000, President Bill Clinton claimed that the agreement then being negotiated to allow China into the WTO would create “a win-win result for both countries.” Exports to China “now support hundreds of thousands of American jobs,” said Clinton, and these figures “can grow substantially with the new access to the Chinese market the WTO agreement creates” (Clinton 2000, 9–10).

China’s entry into the WTO in 2001 was supposed to bring it into compliance with an enforceable, rules-based regime that would require China to open its markets to imports from the United States and other nations by reducing Chinese tariffs and addressing nontariff barriers to trade. Promoters of liberalized U.S.–China trade argued that the United States would benefit because of increased exports to a large and growing consumer market in China. The United States also negotiated a series of special safeguard measures designed to limit the disruptive effects of surging imports from China on domestic producers.

However, China’s trade-distorting practices, aided by China’s currency manipulation and misalignment and its suppression of wages and labor rights, resulted in a flood of dumped and subsidized imports that greatly exceeded the growth of U.S. exports to China. These trade-distorting practices included extending large subsidies to industries such as steel, glass, paper, concrete, and renewable energy industries and rapidly growing its state-owned enterprises, both of which generated a massive buildup of excess capacity in a range of these sectors. This excess capacity created a supply of goods far exceeding Chinese consumer demand, and China dealt with the oversupply by dumping the exports elsewhere, primarily in the United States (Scott 2017a).

The promised surge of U.S. exports to China was also hampered by China’s failure to implement certain policies to increase domestic demand for goods, including goods produced by trading partners. Specifically, for China to become a better market for U.S. exports, it needed to stimulate the growth of domestic consumption through policies that would allow workers to organize and bargain collectively, thus raising wages. China also needed to increase domestic consumption through increased social spending and reductions to the country’s massive savings rate (Scott 2017a). Such policies are all elements of a program of domestic, demand-led growth that the United States, other advanced countries, and international agencies have called on China to implement for many years. But none of these policies have been implemented at anywhere near a large enough scale, and China’s national savings rate has actually increased significantly over the past 15 years (Setser 2016; IMF 2018), which has contributed to the growth of U.S. trade deficits (Bernstein 2016).

In addition, the WTO agreement spurred foreign direct investment (FDI) in Chinese enterprises and the outsourcing of U.S. manufacturing plants, which has expanded China’s manufacturing sector at the expense of the United States, thereby affecting the trade balance between the two countries. Finally, the core of the agreement failed to include any protections to maintain or improve labor or environmental standards or to prohibit currency manipulation. (The descriptions in this paragraph derive from Scott 2017a.)

As a result of these forces, the U.S. trade deficit with China soared after China entered the WTO.

Table 1 displays changes in the U.S.–China goods trade deficit and job displacement from 2001 to 2017 (when the term “trade deficit” is used in this report, it always refers to the goods trade deficit). As the table shows, imports from China increased dramatically in this period, rising from $102.3 billion in 2001 to $505.6 billion in 2017.2 U.S. exports to China rose at a rapid rate from 2001 to 2017, but from a much smaller base, from $19.2 billion in 2001 to $130.4 billion in 2017. As a result, China’s exports to the United States in 2017 (“U.S. general imports”) were nearly four times greater than U.S. exports to China. These trade figures make the China trade relationship the United States’ most imbalanced trade relationship by far (authors’ analysis of USITC 2018).

Table 1

U.S.–China goods trade and job displacement, 2001–2017

Change ($billions)

Percent change

2001

2008

2017

2001–2017

2008–2017

2001–2017

2008–2017

U.S. goods trade with China ($billions, nominal)

U.S. total exports*

$19.2

$71.5

$130.4

$111.1

$58.9

577.8%

82.4%

U.S. general imports

$102.3

$337.8

$505.6

$403.3

$167.8

394.3%

49.7%

U.S. trade balance

‑$83.0

‑$266.3

-$375.2

-$292.2

-$108.9

351.8%

40.9%

Average annual change in the trade balance

-$18.26

-$15.56

9.9%

Change (thousands of jobs)

Percent change

U.S. trade-related jobs supported and displaced (thousands of jobs)

U.S. total exports—jobs supported

179.2

564.2

959.1

780.0

395.0

435.3%

70.0%

U.S. general imports—jobs displaced

1,170.7

3,616.9

5,311.3

4,140.6

1,694.4

353.7%

46.8%

U.S. trade deficit—net jobs displaced

991.5

3,052.7

4,352.2

3,360.6

1,299.4

338.9%

42.6%

Average annual change in net jobs displaced

210.0

185.6

9.7%

* Total exports as reported by the U.S. International Trade Commission include re-exports. The employment estimates shown here are based on total exports. See note 2 for additional details.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Overall, the U.S. goods trade deficit with China grew from $83.0 billion in 2001 to $375.2 billion in 2017, an increase of $292.2 billion. Put another way, since China entered the WTO in 2001, the U.S. trade deficit with China has increased annually by $18.3 billion, or 9.9 percent, on average. Although not shown in the table, we can also examine the trade deficit in another way—not by how much it grew annually, but by adding up what the total deficit was each year to produce a cumulative figure. The data reveal that the cumulative U.S. trade deficit with China over the 2002–2017 (post-WTO) era was $4.2 trillion (USITC 2018 and authors’ calculations).

Between 2008 and 2017, the U.S. goods trade deficit with China increased $108.9 billion. This 40.9 percent increase occurred despite the Great Recession–induced collapse in world trade between 2008 and 2009 and the 23.4 percent decline in the U.S. trade deficit with the rest of the world between 2008 and 2017. As a result, China’s share of the overall U.S. goods trade deficit increased from 32.2 percent in 2008 to 46.5 percent in 2017. (The figures in this paragraph derive from the authors’ analysis of USITC 2018 and U.S. Census Bureau 2018c.)

The growing trade deficit with China has led to U.S. job losses

Each $1 billion in exports to another country from the United States supports some American jobs. However, each $1 billion in imports from another country leads to job loss—by eliminating existing jobs and preventing new job creation—as imports displace goods that otherwise would have been made in the United States by domestic workers.3 The net employment effect of trade depends on the changes in the trade balance. An improving trade balance can support job creation, but a growing trade deficit usually results in growing net U.S. job displacement. The net change in the U.S.–China trade balance between 2001 and 2017 also reflects the effect of trade in intermediate products between the two countries on net trade flows and job losses.

This is what has occurred with China since it entered the WTO; the United States’ widening trade deficit with China has been costing U.S. jobs. While some imports of parts and components from China have gone into the production of final goods, some of which have then been exported to China and the rest of the world, the overall U.S. trade deficit in manufactured products with China and the rest of the world has grown substantially since China entered the WTO.

This paper describes the net effect of the growing U.S.–China goods trade deficit (hereafter referred to as the U.S.–China trade deficit) on employment as jobs “lost or displaced,” with the terms “lost” and “displaced” used interchangeably.4 The employment impacts of the growing U.S. trade deficit with China are estimated in this paper using an input-output model that estimates the direct and indirect labor requirements of producing output in a given domestic industry. The model includes 205 U.S. industries, 76 of which are in the manufacturing sector (see the box titled “Trade and employment models,” as well as the appendix, for details on model structure and data sources). The Bureau of Labor Statistics Employment Projections program (BLS-EP) revised and updated its labor requirements model and related data in October 2017 (BLS-EP 2017a, 2017b). Our models have been revised and updated for this report using the latest available data.5

Scott 2017a estimated jobs lost or displaced due to the growth in the U.S.–China trade deficit from 2001 to 2015. The total job losses reported for 2001 to 2017 in Table 1 in this report are not significantly different than the job losses for 2001 to 2015 reported in Scott 2017a, despite a small increase in the trade deficit since 2015. This is primarily caused by changes in the structure of industry-specific price deflators from the Bureau of Labor Statistics (BLS-EP 2017b). In Scott 2017a, the deflators had a base year of 2005 (the price index is set to 1,000 in the base year). However, in their latest update (BLS-EP 2017b), BLS uses a base year of 2009. There are also some minor revisions in the most recent updates to the deflators that cause the real value of imports and exports to vary from previous years.6 Finally, deflators for 2017 have not yet been published by BLS. In the past, producer price indexes from BLS were used to extrapolate the deflators to the most recent year. In this version of the report, we use the 2026 price projections published by BLS to estimate deflators for 2017, by interpolation. Specifically, the annualized percent change between the 2016 and the 2026 price projections for each industry is applied to the deflator for 2016, to estimate price levels in 2017.

Trade and employment models

The Economic Policy Institute and other researchers have examined the job impacts of trade in recent years by subtracting the job opportunities lost to imports from those gained through exports. That general approach is used in this report. Specifically, this report uses standard input-output models and data to estimate the jobs displaced by trade. Many economists in the public and private sectors have used this type of all-but-identical methodology to estimate jobs gained or displaced by trade, including Groshen, Hobijn, and McConnell (2005) of the Federal Reserve Bank of New York and Bailey and Lawrence (2004) in the Brookings Papers on Economic Activity. The U.S. Department of Commerce has published estimates of the jobs supported by U.S. exports (Tschetter 2010). That study uses input-output and “employment requirements” tables from the Bureau of Labor Statistics Employment Projections program (earlier editions of BLS-EP 2017a), the same source used to develop job displacement estimates in this report. The Tschetter report represents the work of a panel of experts from 20 federal agencies.7

The model estimates the amount of labor (number of jobs) required to produce a given volume of exports and the labor displaced when a given volume of imports is substituted for domestic output. The difference between these two numbers is essentially the jobs displaced by the growing trade deficit, holding all else equal.

Jobs displaced by the United States’ growing trade deficit with China are a net drain on employment in trade-related industries, especially those in manufacturing. Even if increases in demand in other sectors absorb all the workers displaced by trade (which is unlikely), job quality will likely suffer because many nontraded industries such as retail trade and home health care pay lower wages and have less comprehensive benefits than traded-goods industries (Scott 2013, 2017a).

As shown in the bottom panel of Table 1, U.S. exports to China in 2001 supported 179,200 jobs, but U.S. imports displaced production that would have supported 1,170,700 jobs. Therefore, the $83.0 billion trade deficit in 2001 displaced 991,500 jobs in that year. Net job displacement rose to 3,052,700 jobs in 2008 and 4,352,200 jobs in 2017. As a result, since China’s entry into the WTO in 2001 and through 2017, the increase in the U.S.–China trade deficit eliminated or displaced 3,360,600 U.S. jobs. Also shown in Table 1, the U.S. trade deficit with China increased by $108.9 billion (or 40.9 percent) between 2008 and 2017. During that period, the number of jobs displaced increased by 1,299,400 (or 42.6 percent).

For comparative purposes, the growth of the U.S.–China trade deficit between 2001 and 2017 represents a direct loss of 1.5 percent of U.S. GDP in 2017 (authors’ analysis of BEA 2018). Using a macroeconomic model with standard economic multipliers (see Appendix: Methodology in Scott and Glass 2016 for further details) yields an estimate of 3.2 million jobs displaced by a trade deficit of this magnitude, providing further support for the job displacement estimates shown in Table 1.8

Total jobs lost or displaced between 2008 and 2017 alone amounted to 1,299,400, either by the elimination of existing jobs or by the prevention of new job creation through the displacement of domestic production by imports.

The total number of jobs displaced by the growing U.S.–China trade deficit, as estimated here, is thus directly proportional to the size of the total bilateral deficit, which has increased steadily throughout the 2001–2017 period, except for a sharp decline in the recession year of 2009 and a much smaller drop in 2016. Figure A shows visually how rising trade deficits have displaced a growing number of jobs every year since China joined the WTO, with the exception of 2009 (during the Great Recession) and 2016 (during a brief lull in imports from China). On average, 210,000 jobs per year have been lost or displaced since China’s entry into the WTO (as shown in Table 1, last row, data column four).

Figure A

U.S. jobs displaced by the growing goods trade deficit with China since 2001 (in thousands of jobs)

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

The continuing growth of job displacement between 2008 and 2017 slightly outpaced the increase in the bilateral trade deficit in this period because of the relatively rapid growth of U.S. imports of computer and electronic parts from China, discussed below, and the fact that the price index for most of these products fell continuously throughout the study period. The share of U.S. imports from China accounted for by computer and electronic parts (in current, nominal dollars) increased from 32.0 percent in 2008 to 36.5 percent in 2017 (according to the authors’ analysis of USITC 2018).

Unfortunately, growing job losses due to outsourcing and growing trade deficits with China are only part of the story.

Next we turn to analysis of direct China trade and job loss in more detail.

The trade deficit and job losses, by industry

The composition of imports from China is changing in fundamental ways, with significant, negative implications for certain kinds of high-skill, high-wage jobs once thought to be the hallmark of the U.S. economy. Since it entered the WTO in 2001, China has moved rapidly “upscale,” from low-tech, low-skill, labor-intensive industries such as apparel, footwear, and basic electronics to more capital- and skills-intensive industries such as computers, electrical machinery, and motor vehicle parts. It has developed a rapidly growing trade surplus in these specific industries and in high-technology products in general.

Table 2 provides a snapshot of the changes in U.S.–China goods trade flows between 2001 and 2017, by industry, for imports, exports, and the trade balance. The rapid growth of the bilateral trade deficit in computer and electronic parts (including computer and peripheral equipment, semiconductors, and audio and video equipment) accounted for 50.7 percent of the $292.1 billion increase in the U.S. trade deficit with China between 2001 and 2017. In 2017, the total U.S. trade deficit with China was $375.2 billion—$167.3 billion of which was in computer and electronic parts (trade flows by industry in 2001 and 2017 are shown in Supplemental Table 1, available at the end of this document).

Table 2

Change in U.S. goods trade with China, by industry, 2001–2017

U.S. imports

U.S. exports

Trade balance

Industry*

Change ($billions, nominal)

Share of total change

Change ($billions, nominal)

Share of total change

Change ($billions, nominal)

Share of total change

Total change

$403.2

100.0%

$111.1

100.0%

$-292.1

100.0%

Agriculture, forestry, fishing, and hunting

2.3

0.6%

17.3

15.6%

15.0

-5.1%

Mining

-0.1

0.0%

8.5

7.7%

8.6

-3.0%

Oil and gas

0.1

0.0%

6.8

6.2%

6.8

-2.3%

Minerals and ores

0.1

0.0%

1.7

1.5%

1.6

-0.5%

Manufacturing

400.8

99.4%

80.0

72.0%

-320.8

109.8%

Nondurable goods

44.1

10.9%

3.5

3.2%

-40.6

13.9%

Food

3.2

0.8%

2.5

2.3%

-0.6

0.2%

Beverage and tobacco products

0.1

0.0%

0.2

0.2%

0.1

0.0%

Textile mills and textile product mills

11.8

2.9%

0.4

0.4%

-11.4

3.9%

Apparel

20.7

5.1%

0.1

0.1%

-20.7

7.1%

Leather and allied products

8.3

2.0%

0.3

0.3%

-7.9

2.7%

Industrial supplies

44.5

11.0%

20.3

18.3%

-24.2

8.3%

Wood products

3.1

0.8%

1.8

1.6%

-1.3

0.4%

Paper

2.6

0.6%

2.2

2.0%

-0.4

0.1%

Printed matter and related products

2.1

0.5%

0.1

0.1%

-2.0

0.7%

Petroleum and coal products

0.5

0.1%

1.1

1.0%

0.7

-0.2%

Chemicals

16.4

4.1%

12.9

11.7%

-3.4

1.2%

Plastics and rubber products

14.6

3.6%

1.5

1.3%

-13.1

4.5%

Nonmetallic mineral products

5.4

1.3%

0.7

0.6%

-4.7

1.6%

Durable goods

312.2

77.4%

56.2

50.6%

-230.5

78.9%

Primary metals

3.6

0.9%

2.0

1.8%

-1.6

0.5%

Fabricated metal products

18.8

4.7%

2.1

1.9%

-16.8

5.7%

Machinery

30.5

7.6%

6.8

6.1%

-23.7

8.1%

Computer and electronic parts

160.0

39.7%

11.8

10.6%

-148.2

50.7%

Computer and peripheral equipment

50.4

12.5%

0.7

0.7%

-49.7

17.0%

Communications, audio, and video equipment

81.3

20.2%

1.1

0.9%

-80.3

27.5%

Navigational, measuring, electromedical, and control instruments

6.1

1.5%

4.6

4.1%

-1.5

0.5%

Semiconductor and other electronic components, and reproducing magnetic and optical media

22.2

5.5%

5.4

4.9%

-16.8

5.7%

Electrical equipment, appliances, and components

34.2

8.5%

2.8

2.5%

-31.4

10.8%

Transportation equipment

17.2

4.3%

26.6

24.0%

9.4

-3.2%

Motor vehicles and motor vehicle parts

14.8

3.7%

12.9

11.6%

-1.9

0.6%

Aerospace products and parts

0.9

0.2%

13.7

12.3%

12.8

-4.4%

Railroad, ship, and other transportation equipment

1.6

0.4%

0.1

0.1%

-1.5

0.5%

Furniture and related products

18.6

4.6%

0.2

0.1%

-18.4

6.3%

Miscellaneous manufactured commodities

29.3

7.3%

3.9

3.5%

-21.0

7.2%

Scrap and secondhand goods

0.2

0.1%

5.3

4.8%

5.1

-1.7%

* Excludes utilities, construction, and service sectors, which reported no goods trade in this period, and information, which reported negligible goods trade in this period.

Source: Authors’ analysis of U.S. International Trade Commission 2018. For a more detailed explanation of the data sources and computations, see the appendix.

As evident in the increasing trade deficit and also shown in Table 2, imports from China far exceeded exports to China between 2001 and 2017. Table 2 further shows that the growth in manufactured goods imports explained virtually all (99.4 percent) of total growth in imports from China between 2001 and 2017 and included a wide array of products. Computer and electronic parts were responsible for 39.7 percent of the growth in imports in this period, including computer equipment ($50.4 billion, or 12.5 percent of the overall growth in imports) and communications, audio, and video equipment ($81.3 billion, or 20.2 percent). Other major importing sectors included electrical equipment ($34.2 billion, or 8.5 percent), machinery ($30.5 billion, or 7.6 percent), apparel ($20.7 billion, or 5.1 percent) and miscellaneous manufactured commodities ($29.3 billion, or 7.3 percent).

As Table 2 shows, manufacturing was also the top sector exporting to China—72.0 percent of the growth in exports to China between 2001 and 2017 was in manufactured goods, totaling $80.0 billion. Within manufacturing, key export-growth industries included chemicals ($12.9 billion, or 11.7 percent of the growth in exports), aerospace products and parts ($13.7 billion, or 12.3 percent), motor vehicles and parts ($12.9 billion, or 11.6 percent), computer and electronic parts ($11.8 billion, or 10.6 percent), and machinery ($6.8 billion, or 6.1 percent). Scrap and secondhand goods industries—which support no jobs, according to the models used in this report (BLS-EP 2017a)9—accounted for 4.8 percent ($5.3 billion) of the growth in exports.

Agricultural exports—which were dominated by corn, soybeans, and other cash grains—grew faster than any individual manufacturing industry except for transportation equipment, increasing $17.3 billion (15.6 percent of the total increase) between 2001 and 2017. Nonetheless, the overall scale of U.S. total exports to China in 2017 was dwarfed by imports from China in that year, which exceeded the value of exports by nearly 4 to 1, as shown in Table 1.

The import data in Table 2 reflect China’s rapid expansion into higher-value-added commodities once considered strengths of the United States, such as computer and electronic parts, which accounted for 36.5 percent ($184.4 billion) of U.S. imports from China in 2017 (as shown in Supplemental Table 1). This growth is apparent in the shifting trade balance in advanced technology products (ATP), a broad category of high-end technology goods trade tracked by the U.S. Census Bureau (but not broken out in Table 2, which uses U.S. International Trade Commission data).10 ATP includes the more advanced elements of the computer and electronic parts industry as well as other sectors such as biotechnology, life sciences, aerospace, nuclear technology, and flexible manufacturing. The ATP sector includes some auto parts; China is one of the top suppliers of auto parts to the United States, having surpassed Germany (Scott and Wething 2012).

In 2017, the United States had a $135.4 billion trade deficit with China in ATP, reflecting a tenfold increase from $11.8 billion in 2002.11 This ATP deficit was responsible for 36.1 percent of the total U.S.–China trade deficit in 2017. It dwarfs the $25.0 billion surplus in ATP that the United States had with the rest of the world in 2017. As a result of the U.S. ATP deficit with China, the United States ran an overall deficit in ATP products in 2017 (of $110.4 billion), as it has in every year since 2002 (U.S. Census Bureau 2018b).

Job loss or displacement by industry is directly related to trade flows by industry, as shown in Table 3.12 The growing trade deficit with China eliminated 2,500,500 manufacturing jobs between 2001 and 2017, nearly three-fourths (74.4 percent) of the total. By far the largest job displacements occurred in the computer and electronic parts industry, which lost 1,209,900 jobs (36.0 percent of the 3.4 million jobs displaced overall). This industry includes computer and peripheral equipment (661,300 jobs lost, or 19.7 percent of the overall jobs displaced), semiconductors and components (284,200 jobs, or 8.5 percent), and communications, audio, and video equipment (247,800 jobs, or 7.4 percent).

Table 3

Net U.S. jobs created or displaced by goods trade with China, by industry, 2001–2017

Total

Share of total jobs displaced

Total*

-3,360,600

Subtotal, nonmanufacturing

-860,100

25.6%

Subtotal, manufacturing

-2,500,500

74.4%

Agriculture, forestry, fishing, and hunting

76,500

-2.3%

Mining

1,300

0.0%

Oil and gas

4,400

-0.1%

Minerals and ores

-3,000

0.1%

Utilities

-9,500

0.3%

Construction

-13,500

0.4%

Manufacturing

-2,500,500

74.4%

Nondurable goods

-332,900

9.9%

Food

-6,400

0.2%

Beverage and tobacco products

0

0.0%

Textile mills and textile product mills

-119,100

3.5%

Apparel

-169,000

5.0%

Leather and allied products

-38,500

1.1%

Industrial supplies

-226,700

6.7%

Wood products

-29,600

0.9%

Paper

-24,000

0.7%

Printed matter and related products

-28,400

0.8%

Petroleum and coal products

-900

0.0%

Chemicals

-32,400

1.0%

Plastics and rubber products

-78,700

2.3%

Nonmetallic mineral products

-32,800

1.0%

Durable goods

-1,940,800

57.8%

Primary metals

-53,200

1.6%

Fabricated metal products

-144,100

4.3%

Machinery

-108,700

3.2%

Computer and electronic parts

-1,209,900

36.0%

Computer and peripheral equipment

-661,300

19.7%

Communications, audio, and video equipment

-247,800

7.4%

Navigational, measuring, electromedical, and control instruments

-16,600

0.5%

Semiconductors and other electronic components, and reproducing magnetic and optical media

-284,200

8.5%

Electrical equipment, appliances, and components

-145,300

4.3%

Transportation equipment

-17,800

0.5%

Motor vehicles and motor vehicle parts

-44,700

1.3%

Aerospace products and parts

32,500

-1.0%

Railroad, ship, and other transportation equipment

-5,700

0.2%

Furniture and related products

-135,200

4.0%

Miscellaneous manufactured commodities

-126,600

3.8%

Wholesale trade

-184,000

5.5%

Retail trade

-43,200

1.3%

Transportation and warehousing

-92,700

2.8%

Information

-43,100

1.3%

Finance and insurance

-62,600

1.9%

Real estate and rental and leasing

-12,300

0.4%

Professional, scientific, and technical services

-104,400

3.1%

Management of companies and enterprises

-122,900

3.7%

Administrative and support and waste management and remediation services

-161,700

4.8%

Education services

-1,800

0.1%

Healthcare and social assistance

-1,600

0.0%

Arts, entertainment, and recreation

-10,100

0.3%

Accommodation and food services

-34,400

1.0%

Other services (except public administration)

-26,700

0.8%

Public administration

-13,600

0.4%

* Subcategory and overall totals may vary slightly due to rounding.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Several service industries, which provide key inputs to traded-goods production, experienced significant job displacement, including administrative and support and waste management and remediation services (161,700 jobs, or 4.8 percent of overall jobs displaced) and professional, scientific, and technical services (104,400 jobs, or 3.1 percent).

These job displacement estimates are based on changes in the real value of exports and imports. For example, while the share of U.S. imports accounted for by computer and electronic parts from China rose from 23.8 percent in 2001 to 36.5 percent in 2017 (to $184.4 billion, as shown in Supplemental Table 1), the average price indexes (deflators) for most of these products fell sharply between 2001 and 2017—47.4 percent on a trade-weighted basis. Thus, the real value of computer and electronic parts imports increased more than twelvefold in this period, rising from $16.3 billion in 2001 to $208.7 billion in 2017 in constant 2009 dollars (authors’ analysis of real trade flows; see the methodology appendix for data sources and computational details).13

Missed opportunities to create more jobs through fair trade with China

The trade and jobs analysis in this report is focused on the actual jobs gained and lost due to increased trade with China over the past 16 years. This raises the question of what trade and employment could have looked like but for the massive growth of the U.S. trade deficit with China between 2001 and 2017. A full analysis of such scenarios at the level of employment impacts by industry and geographic area is beyond the scope of this report. It will be the subject of future research. But the broad outlines of one such scenario can be quickly sketched from the trade data in Table 2.

To have maintained a stable trade balance with China between 2001 and 2017, imports would have had to grow less rapidly or exports would have had to grow more rapidly—or some combination of the two. For example, had U.S. export growth to China matched the growth of China’s exports to the United States dollar for dollar between 2001 and 2017, balanced trade would have required roughly a fourfold increase in U.S. exports to China in 2017.14 If actual 2017 exports in each industry (shown in Supplemental Table 1) had increased by this ratio (the specific ratio is 3.88-to-1), then the largest growth in exports would have occurred in transportation equipment ($111.7 billion), agricultural products ($71.0 billion), computer and electronic parts ($61.0 billion), chemicals ($56.6 billion), machinery ($33.6 billion), and food and beverage products ($12.8 billion). In total, U.S. exports to China would have increased by $486.4 billion, $375.2 billion more than they actually did.15

If exports to China had increased at this pace, it would have supported the creation of millions of U.S. manufacturing jobs and prevented much of the collapse of overall U.S. manufacturing employment between December 2001 and December 2017, when 3.2 million U.S. manufacturing jobs were lost (BLS 2018b). This level of growth in U.S. exports to China could not have taken place without major structural changes in China’s trade, industrial, macroeconomic, and labor policies. This analysis does illustrate the potential gains had China trade delivered on the promises made by China trade proponents when China entered the WTO in 2001.

Job losses by state

Growing U.S. trade deficits with China have reduced demand for goods produced in every region of the United States and have led to job displacement in all 50 states and the District of Columbia, as shown in Table 4 and Figure B. (Supplemental Table 2 ranks the states by the number of net jobs displaced, while Supplemental Table 3 ranks the states by jobs displaced as a share of total state jobs and presents the states alphabetically.) Table 4 shows that jobs displaced from 2001 to 2017 due to the growing goods trade deficit with China ranged from 0.29 percent to 3.59 percent of total state employment. The 10 hardest-hit states ranked by job shares displaced were New Hampshire, Oregon, California, Minnesota, North Carolina, Rhode Island, Massachusetts, Vermont, Wisconsin, and Texas. This list includes states with high-tech industries (California, Massachusetts, Minnesota, Oregon, and Texas) and manufacturing states (New Hampshire, North Carolina, Rhode Island, Vermont, and Wisconsin). Job losses in these states ranged from 2.57 percent to 3.55 percent of total state employment. Other traditional manufacturing powers—such as Georgia, Kentucky, Indiana, Illinois, South Carolina, and Tennessee—are among the top 20 hardest-hit states, as is Idaho, also a high-tech hub.

Table 4

Net U.S. jobs displaced due to goods trade deficit with China, by state, 2001–2017 (ranked by jobs displaced as a share of total state employment)

Rank

State

Net jobs displaced

State employment

Jobs displaced as share
of state employment

1

New Hampshire

24,000

675,500

3.55%

2

Oregon

62,900

1,873,900

3.36%

3

California

562,500

16,818,700

3.34%

4

Minnesota

88,300

2,932,100

3.01%

5

North Carolina

130,800

4,415,800

2.96%

6

Rhode Island

14,100

494,500

2.84%

7

Massachusetts

99,100

3,609,500

2.75%

8

Vermont

8,600

314,200

2.74%

9

Wisconsin

78,700

2,945,200

2.67%

10

Texas

314,000

12,224,200

2.57%

11

Indiana

77,900

3,105,300

2.51%

12

Idaho

17,600

716,600

2.46%

13

Illinois

148,200

6,062,400

2.45%

14

South Carolina

50,800

2,091,500

2.43%

15

Kentucky

45,400

1,921,200

2.36%

16

New Jersey

96,700

4,129,100

2.34%

17

Alabama

46,900

2,015,400

2.33%

18

Georgia

103,100

4,453,400

2.32%

19

Tennessee

69,300

3,011,200

2.30%

20

Pennsylvania

136,100

5,948,000

2.29%

21

Arizona

63,400

2,774,000

2.29%

22

Connecticut

38,400

1,681,600

2.28%

23

Colorado

59,500

2,658,700

2.24%

24

Mississippi

25,300

1,152,200

2.20%

25

Ohio

121,400

5,528,600

2.20%

26

Arkansas

26,800

1,239,600

2.16%

27

Michigan

92,400

4,372,500

2.11%

28

Utah

29,100

1,468,700

1.98%

29

New York

183,500

9,523,300

1.93%

30

Oklahoma

31,900

1,662,600

1.92%

31

Maine

11,900

622,800

1.91%

32

Iowa

29,900

1,573,200

1.90%

33

Washington

58,100

3,326,100

1.75%

34

Missouri

49,800

2,868,400

1.74%

35

Virginia

66,200

3,952,100

1.68%

36

Maryland

43,000

2,723,700

1.58%

37

New Mexico

12,800

830,800

1.54%

38

Kansas

21,700

1,403,900

1.54%

39

Florida

125,500

8,569,600

1.46%

40

South Dakota

6,300

434,900

1.44%

41

West Virginia

10,600

745,400

1.42%

42

Nebraska

14,200

1,018,000

1.40%

43

Delaware

6,000

456,200

1.32%

44

Nevada

15,900

1,341,400

1.18%

45

Louisiana

21,200

1,970,800

1.08%

46

Hawaii

6,200

652,800

0.95%

47

Montana

4,200

472,700

0.89%

48

Alaska

2,700

329,100

0.83%

49

North Dakota

3,400

430,700

0.78%

50

Wyoming

2,000

281,700

0.72%

51

District of Columbia

2,300

790,500

0.29%

Total*

3,360,600

146,614,300

2.29%

* Totals may vary slightly due to rounding.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

As shown in Supplemental Table 2, the top 10 states in terms of total jobs lost were California (562,500 jobs), Texas (314,000), New York (183,500), Illinois (148,200), Pennsylvania (136,100), North Carolina (130,800), Florida (125,500), Ohio (121,400), Georgia (103,100), and Massachusetts (99,100).

The map in Figure B shows the broad impact of the growing trade deficit with China across the United States, with no areas exempt from job displacement. The 3.4 million U.S. jobs displaced due to the growing trade deficit with China between 2001 and 2017 represented 2.29 percent of total U.S. employment.

Job losses by congressional district

This study also reports the employment impacts of the growing U.S. goods trade deficit with China in every congressional district and in the District of Columbia. Table 5 lists the top 20 hardest-hit congressional districts (those with the largest job losses as a share of overall district employment). Figure C shows job displacement as a share of total district employment in all 435 congressional districts plus the District of Columbia. (Supplemental Table 4 shows the same data, but ranked by net jobs displaced, and Supplemental Table 5 provides the data sorted alphabetically by state.) Because the largest growth in the goods trade deficits with China from 2001 to 2017 occurred in the computer and electronic parts industry, 18 of the 20 hardest-hit districts were in Arizona, California, Illinois, Massachusetts, Minnesota, New York, Oregon, and Texas, where remaining jobs in that industry are concentrated. Georgia and North Carolina, which suffered considerable job displacement in a variety of manufacturing industries, also each have one district in the top 20 hardest-hit districts.16

Table 5

Twenty congressional districts hardest hit by U.S. goods trade deficit with China, 2001–2017 (ranked by jobs displaced as a share of district employment)

Rank

State

District

Net jobs displaced

District employment (in 2011)

Jobs displaced as a share of district employment

1

California

17

59,500

346,100

17.19%

2

California

18

48,300

344,500

14.02%

3

California

19

38,600

324,000

11.91%

4

Texas

31

34,400

323,000

10.65%

5

Oregon

1

31,600

377,200

8.38%

6

California

15

26,900

336,400

8.00%

7

Georgia

14

17,600

290,700

6.05%

8

Texas

3

21,100

371,200

5.68%

9

Massachusetts

3

20,000

355,400

5.63%

10

California

40

14,800

280,500

5.28%

11

Texas

10

16,900

342,600

4.93%

12

California

52

16,900

350,100

4.83%

13

Illinois

6

17,000

355,600

4.78%

14

California

34

14,600

309,400

4.72%

15

Minnesota

1

16,400

348,200

4.71%

16

California

45

16,100

354,400

4.54%

17

Texas

18

13,700

306,400

4.47%

18

New York

18

14,800

332,100

4.46%

19

Arizona

5

13,500

317,900

4.25%

20

North Carolina

2

12,900

303,800

4.25%

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Specifically, of the 20 hardest-hit districts, eight were in California (in rank order, the 17th, 18th, 19th, 15th, 40th, 52nd, 34th, and 45th), four were in Texas (31st, 3rd, 10th, and 18th), and one each were in Oregon (1st), Georgia (14th), Massachusetts (3rd), Illinois (6th), Minnesota (1st), New York (18th), Arizona (5th), and North Carolina (2nd). Job losses in these districts ranged from 12,900 jobs to 59,500 jobs, and from 4.25 percent to 17.19 percent of total district jobs. These distributions reflect both the size of some states (e.g., California and Texas) and the concentration of the industries hardest hit by the growing U.S.–China trade deficit. We have already mentioned the prevalence of the computer and electronic parts industry in certain states; other industries with a presence in these districts include furniture, textiles, apparel, and other manufactured products.

The three hardest-hit congressional districts were all located in Silicon Valley (South Bay Area) in California, including the 17th Congressional District (encompassing Sunnyvale, Cupertino, Santa Clara, Fremont, Newark, North San Jose, and Milpitas), which lost 59,500 jobs, equal to 17.19 percent of all jobs in the district; the 18th Congressional District (including parts of San Jose, Palo Alto, Redwood City, Menlo Park, Stanford, Los Altos, Campbell, Saratoga, Mountain View, and Los Gatos), which lost 48,300 jobs, or 14.02 percent; and the 19th Congressional District (most of San Jose and other parts of Santa Clara County), which lost 38,600 jobs, or 11.91 percent.17

Although the San Francisco Bay Area has experienced rapid growth over the past decade in software and related industries, this growth has come at the expense of direct employment in the production of computer and electronic parts. The computer and electronic parts manufacturing sector has experienced more actual job losses than any other major manufacturing industry has since China joined the WTO.18 There are substantial questions about the long-run ability of firms in the high-tech sectors to continue to innovate while offshoring most or all of the production in their industries (Shi 2010).

Other research confirms job losses from U.S.–China trade

Recent academic research has confirmed findings in this and earlier EPI research (e.g., Kimball and Scott 2014) that the growing U.S.–China trade deficit has caused significant loss of U.S. jobs, especially in manufacturing.

For example, Acemoglu et al. (2014) find that import competition with China from 1999 to 2011 was responsible for up to 2.4 million net job losses (including direct, indirect, and respending effects).19 This result compares with the finding in this paper that 2.6 million jobs were lost due to growing trade deficits with China between 2001 and 2011, as shown in Figure A. Thus, over a roughly comparable period, Acemoglu et al. estimate an employment impact that is roughly 90 percent as large as the estimate found in this study.20

Further academic confirmation of the impacts of China trade on manufacturing employment is provided by Pierce and Schott (2016). Pierce and Schott use an entirely different estimation technique based on differences in the pre- and post-China WTO entry maximum tariff rates, with and without permanent normal trade relations (PNTR) status, which the United States granted to China in the China–WTO implementing legislation. Pierce and Schott estimate the impacts of changes in U.S. international transactions between 1992 and 2008. They find that the grant of PNTR status to China “reduced relative employment growth of the average industry by 3.4 percentage points…after one year [and] 15.6 percentage points after 6 years” (following the grant of PNTR status to China in 2001). They do not translate percentage-point changes in employment into total jobs displaced, but data on changes in total manufacturing employment in this period provide a base of comparison.

The research in this paper looks at the total loss or displacement of jobs due to the growing trade deficit with China and the number of those lost jobs that are manufacturing jobs. We can check the consistency of this finding with a different approach—looking at the total loss of manufacturing jobs and estimating the number of those job losses that are due to growing trade deficits with China. The United States lost 3.2 million manufacturing jobs between December 2001 and December 2017, a decline of 20.0 percent in total manufacturing employment (BLS 2018b). Drawing from Pierce and Schott 2016 above, if 15.6 percentage points of this 20.0 percent decline can be attributed to the growth of the U.S. trade deficit with China, this implies that about 77.7 percent (or 2.5 million) of the manufacturing jobs lost in this period were lost due to the growing trade deficit with China. This estimate is identical to this study’s estimated total manufacturing jobs displaced by the growing U.S.–China trade deficit (2.5 million net jobs displaced). Thus, two other recent academic studies have concluded that the growing U.S.–China trade deficit is responsible for the displacement of at least 2 million U.S. manufacturing jobs since 1990, with most jobs lost since China entered the WTO in 2001.

Lost wages from the increasing trade deficit with China

Growing trade-related job displacement has several direct and indirect effects on workers’ wages. The direct wage effects are a function of the wages forgone in jobs displaced by growing U.S. imports from China minus wage gains from both jobs added in export-producing industries, versus the (lower) wages paid in alternative jobs in nontraded industries (U.S. workers displaced from traded-goods production in manufacturing industries who find jobs in nontraded goods industries experience permanent wage losses, as discussed below). Standard trade theory assumes that economic integration leads to “gains from trade” as workers move from low-productivity jobs in import-competing industries into higher-productivity jobs in export-competing industries. However, this assumption is proven incorrect in Scott 2013, which shows that import-competing jobs pay better than alternative jobs in export-producing industries. Specifically, Scott examines the gains and losses associated with direct changes in employment caused by growing U.S.–China trade deficits between 2001 and 2011, and finds that jobs displaced by imports from China actually paid 17.0 percent more than jobs exporting to China: $1,021.66 per week in import-competing industries versus $872.89 per week in exporting industries (Scott 2013, 24, Table 9a).21 Therefore, simple trade expansion that increases total trade with no underlying change in the trade balance will result in a net loss to workers as they move from higher-paying jobs in import-competing industries to lower-paying jobs in exporting industries.

Furthermore, jobs in both import-competing and exporting industries paid substantially more than jobs in nontraded industries, which pay $791.14 per week (Scott 2013, Table 9a, 24). Between 2001 and 2011, growing exports to China supported 538,000 U.S. jobs, but growing imports displaced 3,280,200 jobs, for a net loss of 2.7 million U.S. jobs (Scott 2013, Table 5, 13). Thus, not only did workers lose wages moving from import-competing to exporting industries, but 2.7 million workers were displaced from jobs where they earned $1,021.66 per week on average and (if they were lucky enough to find jobs) were mostly pushed into jobs in nontraded industries paying an average of only $791.14 per week (a decline of 22.6 percent). In total, U.S. workers suffered a direct net wage loss of $37 billion per year (Scott 2013, 26, Table 9b) due to trade with China. But the direct wage losses are just the tip of the iceberg.

As shown by Josh Bivens in Everybody Wins, Except for Most of Us (Bivens 2008a, with results updated in Bivens 2013), growing trade with China and other low-wage exporters essentially puts all American workers without a college degree (roughly 100 million workers) in direct competition with workers in China (and elsewhere) making much less. He shows that trade with low-wage countries was responsible for 90 percent of the growth in the college wage premium since 1995 (the college wage premium is the percent by which wages of college graduates exceed those of otherwise-equivalent high school graduates), relative to the wages earned by the 100 million non-college-educated workers. The growth of China trade alone was responsible for more than half of the growth in the college wage premium in that period, Bivens finds. To put these estimates in macroeconomic terms, in 2011, trade with low-wage countries lowered annual wages by 5.5 percent—roughly $1,800 per worker for all full-time, full-year workers without a college degree. To provide comparable economywide impact estimates, assume that 100 million workers without a college degree suffered average losses of $1,800 per year, which yields a total national loss of $180 billion (Scott 2017b). Therefore, the indirect, macroeconomic losses to U.S. workers without college degrees caused by growing trade with low-wage nations were about five times as large as the $37 billion in direct wage losses in 2011 from trade with China, and about 40 times as many workers were affected indirectly due to globalization’s wage-lowering effect (100 million) as were displaced by trade with China (2.7 million).22 And China trade alone was responsible for about 56.8 percent of the increase in the overall college/noncollege wage gap between 1995 and 2011.23

Additionally, Autor, Dorn, and Hanson estimate that rising exposure to low-cost Chinese imports lowers labor force participation and reduces wages in local labor markets; in particular, they find that increased import competition has a statistically significant depressing effect on nonmanufacturing wages (Autor, Dorn, and Hanson 2012, abstract). This confirms the findings of Bivens (2008a, 2013). They also find that “transfer benefits payments for unemployment, disability, retirement, and healthcare also rise sharply in exposed labor markets” and that “for the oldest group (50–64), fully 84% of the decline in [manufacturing] employment is accounted for by the rise in nonparticipation, relative to 71% among the prime-age group and 68% among the younger group” (Autor, Dorn, and Hanson 2012, abstract, 25). Thus, Autor, Dorn, and Hanson find that more than two-thirds of all workers displaced by growing competition with Chinese imports dropped out of the labor force. These results are explained, in part, by the finding that “9.9%…of those who lose employment following an import shock obtain federal disability insurance benefits [Social Security Disability Insurance (SSDI) benefits].” Additionally, “rising import exposure spurs a substantial increase in government transfer payments to citizens in the form of increased disability, medical, income assistance and unemployment benefits.” Moreover, “these transfer payments vastly exceed the expenses of the TAA [Trade Adjustment Assistance] program, which specifically targets workers who lose employment due to import competition” (Autor, Dorn, and Hanson 2012, 25, 30). In Autor and Hanson 2014, the effects are totaled, and they find that “for regions affected by Chinese imports, the estimated dollar increase in per capita SSDI payments is more than 30 times as large as the estimated dollar increases in TAA payments.”

The job and wage losses stemming from the growing U.S.–China trade deficit are real—and also increase demands on the social safety net

Some economists and others in the trade debate have argued that job loss numbers extrapolated from trade flows are uninformative because aggregate employment levels in the United States are set by a broad range of macroeconomic influences, not just by trade flows.24 However, while the trade balance is but one of many variables affecting aggregate job creation, it plays a large role in explaining structural change in employment, especially in the manufacturing sector. As noted earlier, between December 2001 and December 2017, 3.2 million U.S. manufacturing jobs were lost (BLS 2018b). The growth of the U.S. trade deficit with China was responsible for the displacement of 2.5 million manufacturing jobs in this period, or about 78.1 percent of manufacturing jobs lost. Thus, manufacturing job loss due to the growing trade deficit with China accounts for roughly four out of five U.S. manufacturing jobs lost or displaced in this period.

The employment impacts of trade identified in this paper can be interpreted as the “all else equal” effect of trade on domestic employment. The Federal Reserve, for example, may decide to cut interest rates to make up for job losses stemming from deteriorating trade balances (or any other economic influence), leaving net employment unchanged. This, however, does not change the fact that trade deficits by themselves are a net drain on employment. Even if macroeconomic policy is adjusted to offset the negative impact of the growing trade deficit with China on total employment, the structure of production and employment in the United States has been negatively affected (Scott 2017a).

The growing trade deficit with China has clearly reduced domestic employment in traded-goods industries, especially in the manufacturing sector, which has been pummeled by plant closings and job losses. Workers from the manufacturing sector displaced by trade have had particular difficulty securing comparable employment elsewhere in the economy. According to the most recent Bureau of Labor Statistics survey covering displaced workers (BLS 2018a, Table 4), more than one-third (35.3 percent) of long-tenured (employed more than three years) manufacturing workers displaced from January 2015 to December 2017 were not working in January 2018, including 21.7 percent who were not in the labor force, i.e., no longer even looking for work, and 13.7 percent who were unemployed.

As noted above, U.S. workers who were directly displaced by trade with China between 2001 and 2011 lost a collective $37.0 billion in wages as a result of accepting lower-paying jobs in nontraded industries or industries that export to China assuming, conservatively, that those workers are reemployed in nontraded goods industries (Scott 2013).25 Worse yet, growing competition with workers in China and other low-wage countries reduced the wages of all 100 million U.S. workers without a college degree, leading to cumulative losses of approximately $180 billion per year in 2011 (Bivens 2013; Scott 2017b). The lost output of unemployed workers, especially that of labor force dropouts, can never be regained and is one of the larger costs of trade-related job displacement to the economy as a whole.26

Trade Adjustment Assistance (TAA) program is a Department of Labor program to provide retraining and unemployment benefits to certain workers who have been displaced by growing imports. However, new research suggests that significant shares of displaced workers are signing up for disability and retirement benefits, other government income assistance, and government medical benefits, in addition to temporary trade adjustment assistance. Many of these workers, such as those on disability and retirement, are permanently dropping out of the labor force, resulting in permanent income losses to themselves and the economy. TAA benefits represent only a tiny share of the costs of adjustment. Examining only those costs for which workers actually qualify for government benefits, Autor, Dorn, and Hanson (2012, Figure 7 at 32) find that unemployment and TAA benefits represent only 6.3 percent of the total benefit costs associated with a $1,000 increase in imports per worker in “commuting zones” over the 1990–2007 period.27 Given the low level of coverage of social safety net programs in the United States versus other developed countries (such as those in the EU), actual adjustment costs for displaced workers are likely substantially larger than the actual U.S. benefits estimated by Autor, Dorn, and Hanson.

Conclusion

The growing U.S. goods trade deficit with China has displaced millions of jobs in the United States and has contributed heavily to the crisis in U.S. manufacturing employment, which has heightened over the last decade largely due to trade with China. Moreover, the United States is piling up foreign debt, losing export capacity, and facing a more fragile macroeconomic environment.

China and America are locked in destructive, interdependent economic cycles, and both can gain from rebalancing trade and capital flows. Although economic growth in China has been rapid, it is unbalanced and unsustainable. Growth in China slowed to 6.9 percent in 2017, and it is projected to fall to 5.5 percent in 2023 (IMF 2018). China’s economy is teetering on the edge between inflation and a growth slump, and a soft landing is nowhere in sight. China needs to rebalance its economy by becoming less dependent on exports and more dependent on domestic demand led by higher wages and infrastructure spending. It also needs to reduce excessive levels of domestic savings to better align savings levels with domestic investment and government borrowing. The best ways to do this are to raise wages and to increase public spending on pensions, health care, and other aspects of the safety net. This will reduce private saving and increase Chinese domestic demand for both domestic and imported goods, reducing China’s trade surplus (Scott 2017a).

The effects on the United States of China’s destructive, rapidly growing trade surplus are outlined in this report. To summarize, the growing U.S. trade deficit with China has eliminated 3.4 million U.S. jobs between 2001 and 2017, including 1.3 million jobs lost since 2008 (the first full year of the Great Recession). Nearly three-fourths of the jobs lost were in manufacturing. These losses were responsible for a substantial share of the 3.2 million U.S. manufacturing jobs lost between December 2001 and December 2017. The growing trade deficit with China has reduced wages of those directly displaced by $37 billion through 2011 alone, and it is largely responsible for the loss of nearly $2,000 per worker per year, due to wage suppression, for all non-college-educated workers in the United States. These losses have been extremely costly for the workers and communities affected, as shown in this report.

The U.S.–China trade relationship needs to undergo a fundamental change. Addressing unfair trade, weak labor, and environmental standards in China, and ending currency manipulation and misalignment, should be our top trade and economic priorities with China (Scott 2017a).

About the authors

Robert E. Scott joined the Economic Policy Institute in 1996 and is currently director of trade and manufacturing policy research. His areas of research include international economics, the impacts of trade and manufacturing policies on working people in the United States and other countries, the economic impacts of foreign investment, and the macroeconomic effects of trade and capital flows. He has published widely in academic journals and the popular press, including in the Journal of Policy Analysis and Management, the International Review of Applied Economics, and the Stanford Law and Policy Review, as well as the Los Angeles Times, Newsday, USA Today, The Baltimore Sun, The Washington Times, and other newspapers. He has also provided economic commentary for a range of electronic media, including NPR, CNN, Bloomberg, and the BBC. He has a Ph.D. in economics from the University of California at Berkeley.

Zane Mokhiber joined EPI in 2016. As a research assistant, he supports the research of EPI’s economists on topics such as wages, labor markets, inequality, trade and manufacturing, and economic growth. Prior to joining EPI, Zane worked for the Worker Institute at Cornell University as an undergraduate research fellow.

Acknowledgments

The authors thank Josh Bivens, Scott Boos, Lora Engdahl, Riley Ohlson, Scott Paul,and Michael Wessel for comments. This research was made possible by support from the Alliance for American Manufacturing.

Appendix: Methodology

The trade and employment analyses in this report are based on a detailed, industry-based study of the relationships between changes in trade flows and employment for each of approximately 205 individual industries of the U.S. economy, specially grouped into 45 custom sectors,28 and using the North American Industry Classification System (NAICS) with data obtained from the U.S. Census Bureau (2013) and the U.S. International Trade Commission (USITC 2018).

The number of jobs supported by $1 million of exports or imports for each of 205 different U.S. industries is estimated using a labor requirements model derived from an input-output table developed by the BLS-EP (2017a).29 This model includes both the direct effects of changes in output (for example, the number of jobs supported by $1 million in auto assembly) and the indirect effects on industries that supply goods (for example, goods used in the manufacture of cars). So, in the auto industry for example, the indirect impacts include jobs in auto parts, steel, and rubber, as well as service industries such as accounting, finance, and computer programming that provide inputs to the motor vehicle manufacturing companies. This model estimates the labor content of trade using empirical estimates of labor content and goods flows between U.S. industries in a given base year (an input-output table for the year 2001 was used in this study) that were developed by the U.S. Department of Commerce and the BLS-EP. It is not a statistical survey of actual jobs gained or lost in individual companies, or the opening or closing of particular production facilities (Bronfenbrenner and Luce 2004 is one of the few studies based on news reports of individual plant closings).

Nominal trade data are used in this analysis are converted to constant 2009 dollars using industry-specific deflators (see below for further details). This is necessary because the labor requirements table is estimated using price levels in that year. Data on real trade flows are converted to constant 2009 dollars using industry-specific price deflators from the BLS-EP (2017b). Use of constant 2009 dollars is required for consistency with the other BLS models used in this study.

Estimation and data sources

Data requirements

Step 1. U.S.–China trade data are obtained from the U.S. International Trade Commission DataWeb (USITC 2018) in four-digit, three-digit, and two-digit NAICS formats. General imports and total exports are downloaded for each year.

Step 2. To conform to the BLS Employment Requirements tables (BLS-EP 2017a), trade data must be converted into the BLS industry classifications system. For NAICS-based data, there are 205 BLS industries. The data are then mapped from NAICS industries onto their respective BLS sectors.

The trade data, which are in current dollars, are deflated into real 2009 dollars using published price deflators from the BLS-EP (2017b). As noted above, deflators for 2017 have not yet been published by the BLS. In this version of the report, we use the 2026 price projections published by BLS to estimate deflators for 2017, by interpolation. Specifically, the annualized percent change between the 2016 and the 2026 price projection for each sector is applied to the deflator for 2016, to estimate price levels in 2017.

Step 3. Real domestic employment requirements tables are downloaded from the BLS-EP (2017a). These matrices are input-output industry-by-industry tables that show the employment requirements for $1 million in outputs in 2009 dollars. So, for industry i the aij entry is the employment indirectly supported in industry i by final sales in industry j and, where i=j, the employment directly supported.

Analysis

Step 1. Job equivalents. BLS trade data are compiled into matrices. Let [T2001] be the 205×2 matrix made up of a column of imports and a column of exports for 2001. [T2017] is defined as the 205×2 matrix of 2017 trade data. Finally, [T2008] is defined as the 205×2 matrix of 2008 trade data. Define [E2001] as the 205×205 matrix consisting of the real 2001 domestic employment requirements tables. To estimate the jobs displaced by trade, perform the following matrix operations:

[J2001] = [T2001] × [E2001]

[J2008] = [T2008] × [E2001]

[J2017] = [T2017] × [E2001]

[J2001] is a 205×2 matrix of job displacement by imports and jobs supported by exports for each of 205 industries in 2001. Similarly, [J2008] and [J2017] are 205×2 matrices of jobs displaced or supported by imports and exports (respectively) for each of 205 industries in 2008 and 2017, respectively.

To estimate jobs created/lost over certain time periods, we perform the following operations:

[Jnx01-17] = [J2017] − [J2001]

[Jnx01-08] = [J2008] − [J2001]

[Jnx08-17] = [J2017] − [J2008]

Step 2. State-by-state analysis. For states, employment-by-industry data are obtained from the Census Bureau’s American Community Survey (ACS) data for 2011 (U.S. Census Bureau 2013) and are mapped into 45 unique census industries and eight aggregated total and subtotals, for a total of 53 sectors.30 We look at job displacement from 2001 to 2017 so from this point, we use [Jnx01-17]. In order to work with 45 sectors, we group the 205 BLS industries into a new matrix, defined as [Jnew01-17], a 45×2 matrix of job displacement numbers.31 We define [St2011] as the 45×51 matrix of state employment shares (with the addition of the District of Columbia) of employment in each industry. We calculate:

[Stjnx01-17] = [St2011]T [Jnew01-17]

where [Stjnx01-17] is the 45×51 matrix of job displacement/support by state and by industry. To get state total job displacement, we add up the subsectors in each state.

Step 3. Congressional district analysis.Employment by congressional district, by industry, and by state is obtained from the ACS data from 2011, which use geographic codings that match the district boundaries of the 113th, 114th, and 115th Congresses. In order to calculate job displacement in each congressional district, we use the columns in [Stjnx01-17], which represent individual state job-displacement-by-industry estimates, and define them as [Stj01], [Stj02], [Stji]…[Stj51], with i representing the state number and each matrix being 45×1.

Each state has Y congressional districts, so [Cdi] is defined as the 45×Y matrix of congressional district employment shares for each state. Congressional district shares are calculated thus:

[Cdj01] = [Stj01]T [Cd01]

[Cdji] = [Stji]T [Cdi]

[Cdj51] = [Stj51]T [Cd51]

where [Cdji] is defined as the 45xY job displacement in state i by congressional district by industry.

To get total job displacement by congressional district, we add up the subsectors in each congressional district in each state.

Endnotes

1. The World Trade Organization, which was created in 1994, was empowered to engage in dispute resolution and to authorize imposition of offsetting duties if its decisions were ignored or rejected by member governments. It expanded the General Agreement on Tariffs and Trade (GATT) trading system’s coverage to include a huge array of subjects never before included in trade agreements, such as food safety standards, environmental laws, social service policies, intellectual property standards, government procurement rules, and more (Wallach and Woodall 2004).

2. Tables 1 and 2 report U.S. general imports from China as measured by “customs value” (the value of imports as appraised by the U.S. Customs Service) and total exports to China as measured by “free alongside” or FAS value (the value of exports at the U.S. port, including the transaction price, inland freight, insurance, and other charges) to China. News releases from the U.S. Census Bureau and the Commerce Department usually emphasize general imports and total exports. The U.S. Internal Trade Commission (USITC) often refers to this netting out of general imports and total exports as the “broad” measure of the trade balance, as opposed to the “narrow” measure, which relies on imports for consumption and domestic exports. (For an example, see USITC 2014. For an explanation of the difference between general imports and imports for consumption, see the U.S. Census Bureau’s online trade glossary [2018e].) The key difference between these two measures is that total exports, as reported by the U.S. Census Bureau, include foreign exports (re-exports), i.e., goods produced in other countries and shipped through the United States, while domestic exports, as implied by the name, do not include re-exports. While a previous version of this report (Kimball and Scott 2014) relied on the narrow definition, using imports for consumption and domestic exports for the analysis, the broad measure was used in Scott 2017a. For 2017, imports for consumption were $504.0 billion, domestic exports were $120.0 billion, and the reported (narrow) trade balance was $384.0 billion. When we compare the trade deficit and job displacement estimates we obtained using the broad measure with the estimates we would have obtained using the narrow measure, we find the difference to be insignificant. The broad measure delivers an estimate of 3.36 million net jobs displaced in 2017, whereas the narrow measure delivers an estimate of 3.44 million net jobs displaced in 2017 (USITC 2018). In this report, all estimates for trade and jobs gained and lost for prior years are based on the broad measure of the trade balance. Data for individual years, and for the change in net jobs displaced, are reported in Table 1, in Figure A, and in other exhibits in this report.

3. While some small proportion of goods imported from China represent a category of goods that may not be produced in the United States, and thus would be “noncompeting” goods, the model used in this report produces an overall estimate of the net jobs displaced by the growing trade deficit. It is, in essence, an estimate of the jobs displaced by the growth of imports in excess of the growth of exports. Since virtually all U.S. imports from China are manufactured goods, as shown in Table 2 in this report, nearly all could be produced in the United States but for China’s unfair trade and currency policies and its domestic “savings glut” (Setser 2016).

4. The term “displaced” would be appropriate to an economy that was at true full employment, where any displaced worker would immediately take a job in another sector of the economy. However, the workers displaced by goods trade are almost exclusively manufacturing workers, and these workers have not been successfully moving into different parts of the economy in recent years: more than one-third of manufacturing workers who were displaced between 2015 and 2017 and who had previously been employed for at least three years were either unemployed or out of the labor force in January 2018 (BLS 2018a). Thus, trade-related job displacement does result in at least some workers moving to a nonworking status, thus “lost” jobs, even if other workers are reemployed elsewhere in the economy (reemployment would result in a change in the composition, rather than the level, of employment).

5. The BLS updated its Employment Requirements Matrix in October 2017 (BLS-EP 2017a), as it normally does every two years. Those revisions have been taken into account in this update. There are 205 NAICS-based BLS industries in the 2017 BLS update (NAICS stands for North American Industry Classification System). The underlying population data from the American Community Survey used to analyze the geographic impacts of trade-related job loss was last updated in Kimball and Scott 2014, with data from the American Community Survey for the 113th Congress census boundaries, which were redrawn after the 2010 census (U.S. Census Bureau 2013).

6. The shift in the deflator base year from 2005 in the previous report to 2009 in this report significantly reduced our estimates of jobs displaced in the computer and electronic parts industry, because large price declines in this industry and its sectors result in outsized impacts on changes in estimated real trade flows (compared with industries that have experienced lower levels of inflation, such as steel or automobile parts), and those price declines were smaller in 2017 than in 2015 (estimated using a 2005 deflator), due to the use of a 2009 base year for deflators in this report (see also note 13, below). Thus, in Scott 2017a, Table 3, we estimate that 1,238,300 direct jobs were displaced in this sector in the 2001–2015 period, a number greater than the 1,209,900 jobs displaced from 2001 to 2017, as shown in Table 3 of this report. The previous report has a greater job displacement estimate in computer and electronic parts despite the fact that the nominal trade deficit in this sector grew less in the 2001–2015 period (by $140.2 billion, as shown in Scott 2017a, Table 2) than it did in the 2001–2017 period (by $148.2 billion, Table 2 in this report).

7. Updated in Rasmussen 2017. Employment requirements tables in that report are derived from BEA input-output data, which are the primary source of data used to estimate BLS employment requirement tables (BLS-EP 2017a).

8. The macroeconomic model developed in Scott and Glass 2016 assumes that a 1.5 percent decrease in GDP would reduce total direct and indirect U.S. employment by roughly 1.3 percent. There were, on average, 153.3 million people employed in the United States in 2017 (BLS 2018c), thus yielding 2.0 million direct and indirect jobs displaced. The macroeconomic model also assumes a respending multiplier of 0.6 and yields a total of 3.2 million direct and indirect and respending jobs displaced by a trade deficit of this magnitude.

9. Scrap and used or secondhand goods are industries 203 and 204, respectively, in the BLS model, and there are no jobs supported or displaced by the production of or trade in goods in these sectors, according to the BLS model. (The jobs supported or displaced by trade are counted in the year these goods are originally manufactured—that is, when they are new—not when they are traded in the secondhand market.)

10. ATPs are an amalgamation of products from a variety of industries and subsectors within the broad NAICS-based categories shown in Table 2. They consist of 10 categories of products including biotechnology, life science, opto-electronics, information and communications, electronics, flexible manufacturing, advanced materials, aerospace, weapons, and nuclear technology (U.S. Census Bureau 2018a). In total ATP trade with the world in 2017, the United States had exports of $353.9 billion, imports of $464.3 billion, and a trade deficit of $110.4 billion. In total ATP trade with China in 2017, the United States had exports of $35.7 billion, imports of $171.1 billion, and a trade deficit of $135.4 billion. This exceeded the overall U.S. ATP deficit of $110.4 billion. Thus, the United States had an ATP trade surplus with the rest of the world in 2015 of $25.0 billion ($135.4 billion − $110.4 billion) (U.S. Census Bureau 2018b).

11. Data for trade in advanced technology products (ATP) by country are not available before 2002.

12. These results are derived from the trade and employment model described in the appendix to this report.

13. Deflators for many sectors in the computer and electronics parts industry fell sharply between 2001 and 2017 due to rapid productivity growth in those sectors. For example, the price index for computer and peripheral equipment fell from 2,666.4 in 2001 to 760.1 in 2017, a decline of 71.5 percent (the price index is set at 1,000 in 2009, the base year). In order to convert exports or imports of computers and peripheral equipment from nominal to real values for 2017, the nominal value is multiplied by 1,000/760.1 (the price index in year 2017 = 1.32). Thus, the real value of computers and peripheral products, a subset of the computer and electronic parts industry, is 32 percent larger than the nominal value in 2017 (in constant 2009 dollars). Overall, the real value of all computer and electronic parts imports in 2017 exceeded nominal values in that year by 11.2 percent. See the appendix for source notes and deflation procedures used.

14. Total imports from China in 2017 exceeded exports by a factor of 3.88-to-1 (505.6/130.4, as shown in Table 1). Thus, exports to China would have had to be roughly four times larger than they actually were in 2017 to achieve balanced trade with China.

15. Data not shown in Table 2. Authors’ analysis based on the change in exports shown, by industry, and the multiplier referred to in the previous note (3.88), based on analysis of data shown in Supplemental Table 1.

16. The computer and electronic parts industry’s share of all jobs lost due to the growth in the U.S.–China trade deficit from 2001 to 2017 ranged from 54.7 percent in Illinois’s 6th District to 92.3 percent in California’s 17th District (authors’ analysis of U.S. Census Bureau 2013; USITC 2018; BLS-EP 2017a, 2017b), compared with the national average of 36.0 percent of jobs (Table 3). In these states the only exceptions—that is, districts where job losses were concentrated in industries other than computer and electronic parts—were California’s 34th and 40th districts, where jobs losses in the apparel industry were 65.3 percent and 56.3 percent, respectively, of jobs lost in each district (compared with the national average of apparel industry job losses accounting for 5.0 percent of jobs lost due to U.S.–China trade, as shown in Table 3). Georgia is also one of the states that are host to one of the 20 hardest-hit congressional districts; Georgia’s 14th Congressional District’s job losses due to the trade deficit include a very large share of jobs in manufacturing, overall, 88.9 percent of all jobs lost, according to unpublished data available upon request. Nationally, manufacturing accounted for a smaller, 74.4 percent share, of all jobs lost (Table 3). Overall, nearly two-thirds (65.4 percent) of jobs lost in Georgia’s 14th district were in textile mills and textile product mills alone. North Carolina’s 2nd district also suffered a large number of job losses in a wide range of manufacturing industries, totaling 88.9 percent of job losses in that district. These losses were spread over a large number of industries, including computer and peripheral equipment, apparel, textiles, and furniture manufacturing.

17. California’s 17th Congressional District is home to Santa Clara University and corporate offices for Apple, Intel, Yahoo, and eBay (Wikipedia 2018). The 18th Congressional District is home to the headquarters of Google, Netflix, and HP, among others (Eshoo 2018).

18. The term “major manufacturing sector” refers here to employment by three-digit NAICS manufacturing industries. The computer and electronic parts industry lost 1,209,900 of the 3,360,600 U.S. manufacturing jobs lost between December 2001 and December 2017 (Table 3), more than six times as many jobs as were lost as in apparel, the next largest of the hardest-hit three-digit manufacturing industries. Trade-related job losses in these industries, shown in Table 3, reflect both potential jobs displaced by the growth of imports (which represents domestic consumption that could have been supplied by domestically produced goods) and by the failure of exports to grow, resulting in large trade deficits in these products.

19. In earlier research, Autor, Dorn, and Hanson “conservatively estimate” that growing “Chinese import competition…imply a supply-shock driven net reduction in U.S. manufacturing employment of 548 thousand workers between 1990 and 2000, and a further reduction of 982 thousand workers between 2000 and 2007.” They note further that these results are based on microeconomic research “exploiting cross-market variation in import exposure” (Autor, Dorn, and Hanson 2012, 19–20, abstract). These estimates are conservative, for several reasons, as noted by the authors. They fail to account for the overall macroeconomic impacts of growing U.S. trade deficits with China, including the direct and indirect effects of growing China trade deficits on U.S. employment, as noted by Acemoglu et al. (2014). As shown in Table 3, the growing U.S. goods trade deficit with China displaced 2.5 million total manufacturing jobs between 2001 and 2017, and an additional 860,100 nonmanufacturing jobs. Thus, approximately 0.34 nonmanufacturing jobs were displaced for each manufacturing job displaced. Differences in parameter estimates notwithstanding, it is important to note that Autor, Dorn, and Hanson (2012) confirm that growing Chinese import competition is responsible for the displacement of approximately 1.5 million U.S. manufacturing jobs from 1990 to 2007, generally confirming the results of current and earlier EPI research.

20. Acemoglu et al. (2014) examine the impacts of U.S.–China trade from 1999 to 2011. The U.S. trade deficit with China increased from $68.7 billion in 1999 to $83.1 billion in 2001 to $295.2 billion in 2011 (U.S. Census Bureau 2018d). Thus, 93.6 percent of the growth of the U.S. trade deficits with China in the 1999–2011 period occurred after China entered the WTO in 2001.

21. Scott’s 2013 estimates are based on average wages from a three-year pooled sample of workers by industry from 2009–2011. These estimates are not updated in this report.

22. The $180 billion in income is redistributed to college-educated workers in the top third of the labor force and to owners of capital. Bivens and Mishel (2015, Figure C) find that for the period of 1973–2014, the loss in the labor share of income was responsible for 8.9 percentage points of the gap between net productivity and real median hourly compensation (a measure of the growth in inequality in this period).

23. Between 1995 and 2011, growing trade with China was responsible for 51.6 percent of the increase in the college/noncollege wage gap in the United States in this period (Bivens 2013, Table 1), 57.1 percent of this wage gap. Thus, China is responsible for a sizeable majority (56.8 percent) of the overall impact of least-developed-countries (LDC) trade on the noncollege wage gap in this period. This analysis decomposes the overall increase in the wage gap (4.8 percentage points), the share attributable to LDC trade, and the share of LDC trade accounted for by China.

24. One frequent criticism of trade and employment studies is that the growth of imports does not displace domestic production, and thus the claim is that such imports do not actually cost jobs. In addition, some assert that if imports from China fell, they would be replaced by imports from some other low-wage country (see, for example, U.S.–China Business Council 2014). However, important empirical research by Autor, Dorn, and Hanson (2012, 4) has shown that “increased exposure to low-income country imports is associated with rising unemployment, decreased labor-force participation, and increased use of disability and other transfer benefits, as well as with lower wages.” The bottom line is that “trade creates new jobs in exporting industries and destroys jobs when imports replace the output of domestic firms. Because trade deficits have risen over the past decade, more jobs have been displaced by imports than created by exports” (Bivens 2008b, 1).

25. This analysis refers to the wage impacts of net jobs lost due to the growth of the U.S.–China trade deficit between 2001 and 2011. It includes net wage gains in the 538,000 jobs supported by increased employment in export industries, less net wage losses in the 3.2 million jobs displaced by increased imports, assuming that all of the 2.7 million net displaced workers are rehired and receive average earnings in jobs in nontraded goods industries (Scott 2013, Table 9a). It is conservative in the sense that it assumes that all of the net displaced workers are rehired in jobs in nontraded goods industries; it excludes the wage losses absorbed by those displaced workers who are not reemployed (for example, the 35.3 percent of long-tenured workers in manufacturing who had been displaced between January 2015 and December 2017 and were not employed in January 2018, as estimated in the BLS Displaced Worker Survey [BLS 2018a]).

26. These losses can never be regained in that the hours unemployed are a permanent loss to the economy, even if an individual worker later finds employment at wages equal to or higher than predisplacement wages. Unemployment costs are a dead-weight loss to the economy, in the same way that unemployment during a recession generates a permanent loss in national economic output.

27. Autor, Dorn, and Hanson (2012) use an analytic technique that compares employment in import-sensitive industries in various geographic areas at a fairly disaggregated level (roughly, cities or counties), referred to in their research as “commuting zones.” They use these zones and data on imports in each region over the study period to do their statistical analysis.

28. A previous edition of this research used data for 56 industries provided by the ACS (Scott 2012). The BLS-EP consolidated several industries, including textiles and apparel, which required us to consolidate data for these industries in our ACS state and congressional district models. Other “not elsewhere classified” industries were consolidated with other industries (e.g., “miscellaneous manufacturing”) or deleted (e.g., in the case of “not specified metal industries”) to update and refine the crosswalk from BLS-EP to ACS industries. As a result of these consolidations, there are 45 industries in the ACS data set used for this study. The current (BLS-EP 2017a) iteration of the employment requirements tables used in this study breaks the economy down into 205 industries, including 76 manufacturing industries. The previous iteration of employment requirements tables, used in Scott 2017a, broke the economy down into 195 industries, including 77 manufacturing industries. The apparel industry and the leather and allied products industry—NAICS 315 and 316—were consolidated into one sector in the BLS-EP 2017a model. We disaggregated job losses in these two sectors in this report using the results from Scott 2017a.

29. The model includes 205 NAICS industries. The trade data include only goods trade. Goods trade data are available for 85 commodity-based industries, plus information (publishing and software, NAICS industry 51), waste and scrap, used or secondhand merchandise, and goods traded under special classification provisions (e.g., goods imported from and returned to Canada; small, unclassified shipments). Trade in scrap, used, and secondhand goods has no impact on employment in the BLS model. Some special classification provision goods are assigned to miscellaneous manufacturing.

30. The U.S. Census Bureau uses its own table of definitions of industries. These are similar to NAICS-based industry definitions, but at a somewhat higher level of aggregation. For this study, we develop a crosswalk from NAICS to Census industries, and we use population estimates from the ACS for each cell in this matrix.

31. The switch from the 195-45 industry crosswalk to the 205-45 industry crosswalk created one inconsistency. The apparel manufacturing and leather and allied products manufacturing industries were separate in the previous (2013, referenced in Scott 2017a) version of the BLS-EP (2017a) model and were combined into one category in the 205 industry table. However, in the 45 industry table, there are two separate categories for these industries. In order to accurately assign jobs displaced to both industries, we apply the ratio of jobs displaced in these two industries in 2015, from the previous version of this report, to the number of jobs displaced in the (now combined) apparel and leather products industry as specified in the current 205 industry table. These inconsistencies will be addressed in the next revision of this trade and employment model.

U.S. Census Bureau. 2013. “American Community Survey: Special Tabulation over 45 industries, Covering 435 Congressional Districts and the District of Columbia (113th Congress Census Boundaries), Plus State and US Totals Based on ACS 2011 1-year file” [custom tabulation, spreadsheets received March 6, 2013].

Net U.S. jobs displaced due to the goods trade deficit with China, by state, 2001–2017 (ranked by net jobs displaced)

Rank

State

Net jobs displaced

State employment

Jobs displaced as share
of state employment

1

California

562,500

16,818,700

3.34%

2

Texas

314,000

12,224,200

2.57%

3

New York

183,500

9,523,300

1.93%

4

Illinois

148,200

6,062,400

2.45%

5

Pennsylvania

136,100

5,948,000

2.29%

6

North Carolina

130,800

4,415,800

2.96%

7

Florida

125,500

8,569,600

1.46%

8

Ohio

121,400

5,528,600

2.20%

9

Georgia

103,100

4,453,400

2.32%

10

Massachusetts

99,100

3,609,500

2.75%

11

New Jersey

96,700

4,129,100

2.34%

12

Michigan

92,400

4,372,500

2.11%

13

Minnesota

88,300

2,932,100

3.01%

14

Wisconsin

78,700

2,945,200

2.67%

15

Indiana

77,900

3,105,300

2.51%

16

Tennessee

69,300

3,011,200

2.30%

17

Virginia

66,200

3,952,100

1.68%

18

Arizona

63,400

2,774,000

2.29%

19

Oregon

62,900

1,873,900

3.36%

20

Colorado

59,500

2,658,700

2.24%

21

Washington

58,100

3,326,100

1.75%

22

South Carolina

50,800

2,091,500

2.43%

23

Missouri

49,800

2,868,400

1.74%

24

Alabama

46,900

2,015,400

2.33%

25

Kentucky

45,400

1,921,200

2.36%

26

Maryland

43,000

2,723,700

1.58%

27

Connecticut

38,400

1,681,600

2.28%

28

Oklahoma

31,900

1,662,600

1.92%

29

Iowa

29,900

1,573,200

1.90%

30

Utah

29,100

1,468,700

1.98%

31

Arkansas

26,800

1,239,600

2.16%

32

Mississippi

25,300

1,152,200

2.20%

33

New Hampshire

24,000

675,500

3.55%

34

Kansas

21,700

1,403,900

1.54%

35

Louisiana

21,200

1,970,800

1.08%

36

Idaho

17,600

716,600

2.46%

37

Nevada

15,900

1,341,400

1.18%

38

Nebraska

14,200

1,018,000

1.40%

39

Rhode Island

14,100

494,500

2.84%

40

New Mexico

12,800

830,800

1.54%

41

Maine

11,900

622,800

1.91%

42

West Virginia

10,600

745,400

1.42%

43

Vermont

8,600

314,200

2.74%

44

South Dakota

6,300

434,900

1.44%

45

Hawaii

6,200

652,800

0.95%

46

Delaware

6,000

456,200

1.32%

47

Montana

4,200

472,700

0.89%

48

North Dakota

3,400

430,700

0.78%

49

Alaska

2,700

329,100

0.83%

50

District of Columbia

2,300

790,500

0.29%

51

Wyoming

2,000

281,700

0.72%

Total*

3,360,600

146,614,300

2.29%

* Totals may vary slightly due to rounding.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program BLS-EP 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Net U.S. jobs displaced due to the goods trade deficit with China, by state, 2001–2017 (sorted alphabetically)

Rank (by jobs displaced as a share of total)

State

Net jobs displaced

State employment

Jobs displaced as share
of state employment

24

Alabama

46,900

2,015,400

2.33%

49

Alaska

2,700

329,100

0.83%

18

Arizona

63,400

2,774,000

2.29%

31

Arkansas

26,800

1,239,600

2.16%

1

California

562,500

16,818,700

3.34%

20

Colorado

59,500

2,658,700

2.24%

27

Connecticut

38,400

1,681,600

2.28%

46

Delaware

6,000

456,200

1.32%

50

District of Columbia

2,300

790,500

0.29%

7

Florida

125,500

8,569,600

1.46%

9

Georgia

103,100

4,453,400

2.32%

45

Hawaii

6,200

652,800

0.95%

36

Idaho

17,600

716,600

2.46%

4

Illinois

148,200

6,062,400

2.45%

15

Indiana

77,900

3,105,300

2.51%

29

Iowa

29,900

1,573,200

1.90%

34

Kansas

21,700

1,403,900

1.54%

25

Kentucky

45,400

1,921,200

2.36%

35

Louisiana

21,200

1,970,800

1.08%

41

Maine

11,900

622,800

1.91%

26

Maryland

43,000

2,723,700

1.58%

10

Massachusetts

99,100

3,609,500

2.75%

12

Michigan

92,400

4,372,500

2.11%

13

Minnesota

88,300

2,932,100

3.01%

32

Mississippi

25,300

1,152,200

2.20%

23

Missouri

49,800

2,868,400

1.74%

47

Montana

4,200

472,700

0.89%

38

Nebraska

14,200

1,018,000

1.40%

37

Nevada

15,900

1,341,400

1.18%

33

New Hampshire

24,000

675,500

3.55%

11

New Jersey

96,700

4,129,100

2.34%

40

New Mexico

12,800

830,800

1.54%

3

New York

183,500

9,523,300

1.93%

6

North Carolina

130,800

4,415,800

2.96%

48

North Dakota

3,400

430,700

0.78%

8

Ohio

121,400

5,528,600

2.20%

28

Oklahoma

31,900

1,662,600

1.92%

19

Oregon

62,900

1,873,900

3.36%

5

Pennsylvania

136,100

5,948,000

2.29%

39

Rhode Island

14,100

494,500

2.84%

22

South Carolina

50,800

2,091,500

2.43%

44

South Dakota

6,300

434,900

1.44%

16

Tennessee

69,300

3,011,200

2.30%

2

Texas

314,000

12,224,200

2.57%

30

Utah

29,100

1,468,700

1.98%

43

Vermont

8,600

314,200

2.74%

17

Virginia

66,200

3,952,100

1.68%

21

Washington

58,100

3,326,100

1.75%

42

West Virginia

10,600

745,400

1.42%

14

Wisconsin

78,700

2,945,200

2.67%

51

Wyoming

2,000

281,700

0.72%

Total*

3,360,600

146,614,300

2.29%

* Totals may vary slightly due to rounding.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program BLS-EP 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Net U.S. jobs displaced due to the goods trade deficit with China, by congressional district, 2001–2017 (ranked by net jobs displaced)

Rank

State

District

Net jobs displaced

District employment (in 2011)

Jobs displaced as a share of employment

1

California

17

59,500

346,100

17.19%

2

California

18

48,300

344,500

14.02%

3

California

19

38,600

324,000

11.91%

4

Texas

31

34,400

323,000

10.65%

5

Oregon

1

31,600

377,200

8.38%

6

California

15

26,900

336,400

8.00%

7

Georgia

14

17,600

290,700

6.05%

8

Texas

3

21,100

371,200

5.68%

9

Massachusetts

3

20,000

355,400

5.63%

10

California

40

14,800

280,500

5.28%

11

Texas

10

16,900

342,600

4.93%

12

California

52

16,900

350,100

4.83%

13

Illinois

6

17,000

355,600

4.78%

14

California

34

14,600

309,400

4.72%

15

Minnesota

1

16,400

348,200

4.71%

16

California

45

16,100

354,400

4.54%

17

Texas

18

13,700

306,400

4.47%

18

New York

18

14,800

332,100

4.46%

19

Arizona

5

13,500

317,900

4.25%

20

North Carolina

2

12,900

303,800

4.25%

21

Minnesota

3

15,000

353,800

4.24%

22

Texas

2

15,400

364,600

4.22%

23

North Carolina

10

13,600

324,000

4.20%

24

Minnesota

2

15,000

358,300

4.19%

25

Massachusetts

2

14,800

356,500

4.15%

26

Texas

17

13,600

329,300

4.13%

27

North Carolina

8

12,400

301,700

4.11%

28

California

14

14,600

364,000

4.01%

29

California

48

14,100

352,600

4.00%

30

California

49

11,900

299,700

3.97%

31

California

35

11,300

284,800

3.97%

32

North Carolina

13

13,600

349,900

3.89%

33

New Hampshire

2

12,800

332,200

3.85%

34

Texas

25

11,600

302,200

3.84%

35

North Carolina

6

13,100

341,800

3.83%

36

South Carolina

3

10,100

264,500

3.82%

37

California

39

12,600

332,000

3.80%

38

Texas

32

13,600

360,900

3.77%

39

Massachusetts

4

14,000

374,800

3.74%

40

California

46

11,700

314,400

3.72%

41

Illinois

8

13,100

366,300

3.58%

42

Mississippi

1

10,900

305,600

3.57%

43

Indiana

3

11,600

327,000

3.55%

44

North Carolina

5

11,500

324,500

3.54%

45

Alabama

5

10,900

311,900

3.49%

46

Texas

33

9,900

283,900

3.49%

47

Colorado

2

13,400

384,600

3.48%

48

Colorado

4

11,800

344,100

3.43%

49

New Jersey

7

12,900

377,100

3.42%

50

Kentucky

6

11,400

335,400

3.40%

51

Georgia

7

10,600

312,500

3.39%

52

Illinois

10

11,000

324,800

3.39%

53

California

7

10,600

313,200

3.38%

54

Texas

24

13,100

388,600

3.37%

55

Massachusetts

5

12,900

387,400

3.33%

56

California

44

9,000

270,600

3.33%

57

Wisconsin

5

12,300

370,600

3.32%

58

South Carolina

5

9,100

275,200

3.31%

59

Arizona

9

11,800

360,300

3.28%

60

Indiana

8

10,700

329,300

3.25%

61

Alabama

4

8,500

262,900

3.23%

62

Indiana

2

10,200

317,800

3.21%

63

South Carolina

4

9,600

301,000

3.19%

64

Minnesota

6

11,100

348,700

3.18%

65

New Hampshire

1

11,200

352,600

3.18%

66

Illinois

11

11,000

347,300

3.17%

67

Illinois

14

11,100

351,000

3.16%

68

California

13

10,700

340,200

3.15%

69

Wisconsin

6

11,100

353,600

3.14%

70

Washington

3

8,900

284,500

3.13%

71

California

38

9,800

313,300

3.13%

72

Texas

7

11,600

376,300

3.08%

73

New York

25

10,300

335,400

3.07%

74

Michigan

2

9,700

315,900

3.07%

75

Wisconsin

1

10,500

342,500

3.07%

76

North Carolina

12

9,800

319,800

3.06%

77

Wisconsin

3

10,700

353,500

3.03%

78

California

4

8,900

294,200

3.03%

79

Tennessee

7

8,600

285,800

3.01%

80

Ohio

14

10,500

349,700

3.00%

81

Georgia

9

8,500

284,600

2.99%

82

Washington

1

9,900

332,300

2.98%

83

Idaho

1

9,800

329,900

2.97%

84

New York

19

9,700

327,300

2.96%

85

Texas

12

10,000

337,500

2.96%

86

California

32

8,700

293,800

2.96%

87

Oregon

3

11,300

383,300

2.95%

88

North Carolina

11

8,700

295,400

2.95%

89

Georgia

3

8,400

285,800

2.94%

90

Ohio

7

9,600

326,800

2.94%

91

Ohio

4

9,300

317,900

2.93%

92

Tennessee

4

9,200

314,500

2.93%

93

New Jersey

5

10,400

356,100

2.92%

94

Pennsylvania

3

9,100

317,700

2.86%

95

North Carolina

4

10,000

350,900

2.85%

96

Indiana

6

8,800

311,900

2.82%

97

Illinois

4

9,200

326,600

2.82%

98

California

42

8,600

307,000

2.80%

99

Pennsylvania

15

9,600

343,800

2.79%

100

Florida

8

7,900

283,400

2.79%

101

Rhode Island

2

7,200

260,300

2.77%

102

Virginia

9

8,200

298,400

2.75%

103

Tennessee

5

9,700

353,400

2.74%

104

New Jersey

11

9,800

358,800

2.73%

105

Ohio

5

9,100

334,200

2.72%

106

Rhode Island

1

6,800

250,900

2.71%

107

Ohio

13

8,600

320,400

2.68%

108

California

37

9,000

335,600

2.68%

109

Kentucky

2

8,500

317,100

2.68%

110

Illinois

9

9,300

347,200

2.68%

111

North Carolina

9

9,900

371,400

2.67%

112

California

12

10,600

399,400

2.65%

113

Minnesota

5

9,300

352,000

2.64%

114

Kentucky

3

8,800

333,300

2.64%

115

Arkansas

3

8,600

327,000

2.63%

116

Vermont

Statewide

8,600

327,300

2.63%

117

Tennessee

3

7,800

297,000

2.63%

118

Wisconsin

8

9,500

362,800

2.62%

119

Ohio

8

8,600

328,800

2.62%

120

New Jersey

8

9,700

371,000

2.61%

121

Michigan

3

8,200

315,300

2.60%

122

Pennsylvania

8

9,300

357,800

2.60%

123

Pennsylvania

6

9,400

362,300

2.59%

124

Michigan

10

8,000

308,700

2.59%

125

Tennessee

1

7,700

297,600

2.59%

126

Alabama

3

7,100

274,600

2.59%

127

Massachusetts

6

9,600

372,000

2.58%

128

California

43

7,800

302,800

2.58%

129

Georgia

6

9,300

361,200

2.57%

130

Oklahoma

1

9,300

361,900

2.57%

131

New York

22

8,200

320,200

2.56%

132

California

27

8,500

332,200

2.56%

133

Texas

26

9,400

368,300

2.55%

134

Wisconsin

7

8,600

338,400

2.54%

135

California

30

9,100

358,200

2.54%

136

Utah

3

7,900

311,200

2.54%

137

Ohio

16

9,000

355,600

2.53%

138

Iowa

1

9,900

392,300

2.52%

139

New York

2

9,000

357,800

2.52%

140

New Jersey

9

8,500

338,500

2.51%

141

New York

1

8,600

343,300

2.51%

142

Pennsylvania

12

8,300

331,900

2.50%

143

Wisconsin

4

7,700

308,000

2.50%

144

California

50

7,400

296,200

2.50%

145

Connecticut

5

8,700

348,300

2.50%

146

Pennsylvania

10

7,800

312,500

2.50%

147

Pennsylvania

17

7,800

312,600

2.50%

148

Pennsylvania

18

8,600

345,000

2.49%

149

Michigan

11

8,500

342,100

2.48%

150

Michigan

6

7,700

310,400

2.48%

151

California

25

7,500

302,700

2.48%

152

Arizona

6

9,000

366,000

2.46%

153

Arizona

7

6,900

282,300

2.44%

154

California

47

8,000

327,600

2.44%

155

Minnesota

4

8,200

336,000

2.44%

156

California

29

7,400

303,700

2.44%

157

New Jersey

6

8,600

353,600

2.43%

158

Tennessee

6

7,400

304,500

2.43%

159

New York

7

7,800

322,200

2.42%

160

Texas

6

8,400

348,800

2.41%

161

Pennsylvania

16

7,800

327,700

2.38%

162

Pennsylvania

5

7,500

316,800

2.37%

163

Pennsylvania

4

8,100

342,900

2.36%

164

Oregon

5

7,700

326,700

2.36%

165

Utah

4

7,800

331,500

2.35%

166

New York

24

7,700

327,300

2.35%

167

Georgia

11

8,000

340,900

2.35%

168

Indiana

4

7,700

328,500

2.34%

169

Illinois

17

7,300

311,700

2.34%

170

South Carolina

7

6,300

269,400

2.34%

171

Indiana

7

7,300

312,200

2.34%

172

Connecticut

4

8,000

343,000

2.33%

173

Connecticut

3

8,200

352,700

2.32%

174

Kentucky

1

6,600

284,800

2.32%

175

Illinois

5

9,200

397,600

2.31%

176

Colorado

5

7,300

315,900

2.31%

177

Tennessee

8

6,900

299,200

2.31%

178

California

26

7,500

325,900

2.30%

179

Oregon

4

7,100

309,000

2.30%

180

Florida

13

7,100

309,200

2.30%

181

Kansas

3

8,500

370,300

2.30%

182

New York

23

7,400

324,600

2.28%

183

Iowa

2

8,500

373,400

2.28%

184

California

53

7,800

342,700

2.28%

185

Indiana

9

7,700

339,400

2.27%

186

North Carolina

1

6,600

291,800

2.26%

187

California

33

8,200

364,200

2.25%

188

Massachusetts

9

7,900

352,300

2.24%

189

Utah

2

6,800

305,700

2.22%

190

Ohio

6

6,500

292,300

2.22%

191

Colorado

6

8,200

369,600

2.22%

192

Texas

21

8,000

361,200

2.21%

193

Michigan

9

7,200

326,100

2.21%

194

Pennsylvania

7

7,500

339,700

2.21%

195

Michigan

8

7,300

330,800

2.21%

196

Texas

9

7,200

326,400

2.21%

197

Idaho

2

7,800

355,000

2.20%

198

Missouri

2

8,300

378,600

2.19%

199

New York

27

7,400

337,800

2.19%

200

Texas

30

6,400

292,300

2.19%

201

Florida

12

6,200

283,200

2.19%

202

Massachusetts

8

8,200

375,600

2.18%

203

Virginia

10

8,200

376,400

2.18%

204

Florida

23

7,400

339,900

2.18%

205

Illinois

16

7,200

330,800

2.18%

206

Ohio

10

6,800

312,800

2.17%

207

Florida

22

7,200

332,000

2.17%

208

Missouri

7

7,300

337,400

2.16%

209

Minnesota

7

7,100

328,700

2.16%

210

Wisconsin

2

8,400

390,000

2.15%

211

Arkansas

2

7,200

336,300

2.14%

212

Texas

4

6,400

299,300

2.14%

213

Texas

35

6,800

318,200

2.14%

214

California

41

5,800

271,900

2.13%

215

Pennsylvania

9

6,500

304,800

2.13%

216

Michigan

4

6,100

286,300

2.13%

217

Pennsylvania

11

7,000

329,300

2.13%

218

Virginia

5

6,700

316,100

2.12%

219

Texas

29

6,200

292,900

2.12%

220

Utah

1

6,600

312,400

2.11%

221

California

5

6,900

326,800

2.11%

222

Missouri

8

6,300

298,500

2.11%

223

Oklahoma

4

7,400

350,900

2.11%

224

Ohio

9

6,600

315,000

2.10%

225

Pennsylvania

13

7,100

339,000

2.09%

226

Indiana

1

6,500

310,600

2.09%

227

Arizona

8

6,300

301,700

2.09%

228

Georgia

10

6,000

287,400

2.09%

229

Illinois

3

6,600

319,500

2.07%

230

South Carolina

2

6,300

305,600

2.06%

231

Washington

10

6,000

291,300

2.06%

232

Alabama

2

5,700

276,900

2.06%

233

Washington

7

7,800

380,000

2.05%

234

New Mexico

1

6,400

311,900

2.05%

235

Indiana

5

7,300

357,700

2.04%

236

Arkansas

4

6,000

295,100

2.03%

237

California

28

7,300

359,900

2.03%

238

Texas

5

6,100

300,800

2.03%

239

New Jersey

4

6,600

326,400

2.02%

240

California

31

5,900

292,200

2.02%

241

California

20

6,100

302,500

2.02%

242

New Jersey

12

7,100

352,400

2.01%

243

Colorado

7

7,300

362,500

2.01%

244

Minnesota

8

6,100

303,400

2.01%

245

Alabama

6

6,400

318,400

2.01%

246

Kentucky

4

6,700

333,500

2.01%

247

Michigan

7

6,000

299,100

2.01%

248

Tennessee

9

6,100

305,300

2.00%

249

New York

17

6,800

341,400

1.99%

250

Texas

16

5,600

281,300

1.99%

251

Texas

22

7,000

352,500

1.99%

252

California

10

5,500

277,200

1.98%

253

South Carolina

6

5,000

253,500

1.97%

254

Pennsylvania

14

6,300

323,200

1.95%

255

Ohio

12

7,000

359,500

1.95%

256

New Jersey

1

6,600

339,200

1.95%

257

California

6

5,600

288,300

1.94%

258

Georgia

12

5,400

278,200

1.94%

259

Illinois

2

5,400

278,200

1.94%

260

Texas

8

6,000

309,200

1.94%

261

Ohio

1

6,400

332,300

1.93%

262

Illinois

18

6,500

337,500

1.93%

263

Colorado

1

7,400

384,400

1.93%

264

Connecticut

2

6,700

348,600

1.92%

265

California

1

5,000

260,300

1.92%

266

Connecticut

1

6,700

349,800

1.92%

267

Georgia

2

4,800

251,200

1.91%

268

Washington

9

6,500

341,400

1.90%

269

Georgia

4

5,900

311,700

1.89%

270

Maine

2

5,700

302,700

1.88%

271

Virginia

6

6,400

339,900

1.88%

272

New York

21

5,800

309,200

1.88%

273

New York

3

6,300

336,700

1.87%

274

New Jersey

10

5,800

310,700

1.87%

275

Virginia

7

6,800

364,600

1.87%

276

Illinois

15

5,900

316,500

1.86%

277

Michigan

13

4,300

230,700

1.86%

278

Ohio

3

6,200

333,000

1.86%

279

Georgia

5

5,900

318,100

1.85%

280

Ohio

2

6,000

323,600

1.85%

281

California

11

6,000

324,200

1.85%

282

Michigan

5

4,900

264,800

1.85%

283

Ohio

15

6,200

336,400

1.84%

284

Nevada

2

5,700

309,400

1.84%

285

Tennessee

2

6,000

327,200

1.83%

286

Michigan

14

4,700

257,700

1.82%

287

Maine

1

6,200

340,400

1.82%

288

Maryland

6

6,600

363,200

1.82%

289

Ohio

11

5,000

275,200

1.82%

290

New York

12

7,600

418,800

1.81%

291

Arkansas

1

5,000

277,400

1.80%

292

New Jersey

3

6,200

344,200

1.80%

293

Nebraska

2

5,600

316,300

1.77%

294

New York

26

5,800

327,700

1.77%

295

Virginia

4

5,800

327,900

1.77%

296

New York

20

6,300

357,600

1.76%

297

Massachusetts

1

6,000

341,000

1.76%

298

Georgia

13

5,500

312,800

1.76%

299

Pennsylvania

1

4,800

273,300

1.76%

300

Florida

25

5,700

326,000

1.75%

301

California

51

4,500

258,600

1.74%

302

Arizona

2

5,200

299,200

1.74%

303

Missouri

5

6,000

345,300

1.74%

304

Oklahoma

2

5,000

290,300

1.72%

305

New York

6

5,600

327,000

1.71%

306

Mississippi

3

5,200

303,900

1.71%

307

Nebraska

1

5,500

321,700

1.71%

308

Florida

7

5,500

322,500

1.71%

309

Missouri

3

6,300

370,000

1.70%

310

West Virginia

1

4,400

258,700

1.70%

311

Maryland

8

6,800

400,100

1.70%

312

Florida

27

5,300

313,600

1.69%

313

Texas

1

5,000

297,700

1.68%

314

Mississippi

4

5,100

304,900

1.67%

315

New York

5

5,600

336,200

1.67%

316

Alabama

7

4,200

253,500

1.66%

317

Oregon

2

5,200

314,200

1.65%

318

Illinois

7

4,900

298,500

1.64%

319

Oklahoma

5

5,700

348,800

1.63%

320

Florida

16

4,500

276,100

1.63%

321

Missouri

6

5,800

355,900

1.63%

322

Michigan

1

4,700

290,200

1.62%

323

Kansas

2

5,500

339,900

1.62%

324

North Carolina

7

5,100

315,400

1.62%

325

Washington

5

4,700

291,500

1.61%

326

New York

16

5,200

323,600

1.61%

327

Michigan

12

5,000

313,800

1.59%

328

Illinois

1

4,600

290,200

1.59%

329

Mississippi

2

4,200

266,900

1.57%

330

Texas

27

4,800

305,600

1.57%

331

Illinois

12

4,700

301,000

1.56%

332

Florida

14

5,000

320,700

1.56%

333

Maryland

1

5,300

342,300

1.55%

334

Massachusetts

7

5,700

369,800

1.54%

335

Iowa

3

6,000

390,800

1.54%

336

California

9

4,200

275,300

1.53%

337

New York

14

5,200

341,800

1.52%

338

Florida

6

4,300

283,200

1.52%

339

South Dakota

1

6,300

415,600

1.52%

340

Florida

21

4,800

316,800

1.52%

341

Florida

15

4,600

304,200

1.51%

342

New Mexico

3

4,300

284,800

1.51%

343

Texas

36

4,400

291,900

1.51%

344

Virginia

1

5,300

352,400

1.50%

345

Georgia

8

4,100

272,700

1.50%

346

New York

8

4,400

292,700

1.50%

347

New York

10

5,400

360,300

1.50%

348

Florida

20

4,500

302,100

1.49%

349

Alabama

1

4,200

283,000

1.48%

350

New York

13

4,700

317,200

1.48%

351

New York

11

4,700

317,500

1.48%

352

Missouri

1

4,900

331,500

1.48%

353

Missouri

4

4,800

324,900

1.48%

354

Maryland

4

5,600

384,100

1.46%

355

Arizona

4

3,400

233,500

1.46%

356

California

2

4,700

323,100

1.45%

357

Kentucky

5

3,400

234,300

1.45%

358

New York

15

3,700

255,900

1.45%

359

Florida

5

4,100

284,000

1.44%

360

Iowa

4

5,500

382,300

1.44%

361

South Carolina

1

4,300

299,800

1.43%

362

Florida

24

4,200

293,400

1.43%

363

Delaware

Statewide

6,000

420,400

1.43%

364

Florida

4

4,700

329,900

1.42%

365

Virginia

11

5,700

400,900

1.42%

366

New York

9

4,600

324,900

1.42%

367

Florida

18

4,000

284,000

1.41%

368

New Jersey

2

4,500

324,400

1.39%

369

Washington

8

4,400

318,000

1.38%

370

Virginia

2

4,700

339,800

1.38%

371

Maryland

3

5,100

369,500

1.38%

372

Florida

11

3,000

217,400

1.38%

373

Arizona

3

3,600

262,200

1.37%

374

New York

4

4,700

342,500

1.37%

375

Maryland

7

4,300

315,700

1.36%

376

Arizona

1

3,600

264,900

1.36%

377

West Virginia

2

3,600

266,900

1.35%

378

Oklahoma

3

4,400

329,900

1.33%

379

Pennsylvania

2

3,600

273,100

1.32%

380

Illinois

13

4,300

326,600

1.32%

381

California

8

3,100

235,500

1.32%

382

Virginia

3

4,200

320,100

1.31%

383

Florida

26

4,400

335,600

1.31%

384

Maryland

2

4,600

351,700

1.31%

385

Florida

10

4,300

331,500

1.30%

386

Texas

14

3,900

303,300

1.29%

387

Washington

2

4,100

318,900

1.29%

388

Texas

20

4,000

311,400

1.28%

389

Texas

13

3,900

309,000

1.26%

390

Maryland

5

4,600

368,200

1.25%

391

Texas

23

3,600

289,700

1.24%

392

Washington

6

3,400

275,500

1.23%

393

Louisiana

4

3,800

311,100

1.22%

394

Hawaii

1

4,000

330,100

1.21%

395

Colorado

3

4,000

331,400

1.21%

396

Nevada

4

3,300

274,300

1.20%

397

Florida

2

3,600

301,500

1.19%

398

North Carolina

3

3,600

305,600

1.18%

399

Texas

15

3,300

280,900

1.17%

400

California

24

3,800

323,500

1.17%

401

West Virginia

3

2,600

223,000

1.17%

402

Nevada

3

3,900

336,500

1.16%

403

Georgia

1

3,300

286,100

1.15%

404

Kansas

4

3,800

332,900

1.14%

405

Kansas

1

3,900

345,900

1.13%

406

Texas

28

3,000

266,300

1.13%

407

Texas

19

3,500

310,700

1.13%

408

Louisiana

6

4,100

367,800

1.11%

409

Louisiana

1

3,900

354,000

1.10%

410

Texas

11

3,400

308,800

1.10%

411

Louisiana

3

3,600

328,100

1.10%

412

Texas

34

2,600

242,200

1.07%

413

Florida

9

3,400

317,200

1.07%

414

Florida

19

2,800

265,200

1.06%

415

Nevada

1

3,000

284,700

1.05%

416

Nebraska

3

3,100

305,600

1.01%

417

Virginia

8

4,200

423,700

0.99%

418

Florida

1

3,000

303,900

0.99%

419

Louisiana

5

2,800

283,900

0.99%

420

Florida

3

2,700

277,000

0.97%

421

Louisiana

2

3,200

329,000

0.97%

422

California

36

2,400

251,900

0.95%

423

California

22

2,700

289,600

0.93%

424

North Dakota

Statewide

3,400

370,800

0.92%

425

California

3

2,600

286,600

0.91%

426

Montana

Statewide

4,200

480,000

0.88%

427

Washington

4

2,300

284,500

0.81%

428

Alaska

Statewide

2,700

344,300

0.78%

429

New Mexico

2

2,100

273,100

0.77%

430

District of Columbia

Districtwide

2,300

310,600

0.74%

431

Hawaii

2

2,200

299,400

0.73%

432

California

23

2,000

274,100

0.73%

433

California

16

1,700

244,900

0.69%

434

Wyoming

Statewide

2,000

290,000

0.69%

435

Florida

17

1,500

248,700

0.60%

436

California

21

200

243,800

0.08%

Total*

3,360,600

140,400,900

2.39%

* Totals may vary slightly due to rounding.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, and Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

Net U.S. jobs displaced due to the goods trade deficit with China, by congressional district, 2001–2017 (sorted alphabetically by state)

Rank (by jobs displaced as a share of total)

State

District

Net jobs displaced

District employment (in 2011)

Jobs displaced as a share of employment

349

Alabama

1

4,200

283,000

1.48%

232

Alabama

2

5,700

276,900

2.06%

126

Alabama

3

7,100

274,600

2.59%

61

Alabama

4

8,500

262,900

3.23%

45

Alabama

5

10,900

311,900

3.49%

245

Alabama

6

6,400

318,400

2.01%

316

Alabama

7

4,200

253,500

1.66%

428

Alaska

Statewide

2,700

344,300

0.78%

376

Arizona

1

3,600

264,900

1.36%

302

Arizona

2

5,200

299,200

1.74%

373

Arizona

3

3,600

262,200

1.37%

355

Arizona

4

3,400

233,500

1.46%

19

Arizona

5

13,500

317,900

4.25%

152

Arizona

6

9,000

366,000

2.46%

153

Arizona

7

6,900

282,300

2.44%

227

Arizona

8

6,300

301,700

2.09%

59

Arizona

9

11,800

360,300

3.28%

291

Arkansas

1

5,000

277,400

1.80%

211

Arkansas

2

7,200

336,300

2.14%

115

Arkansas

3

8,600

327,000

2.63%

236

Arkansas

4

6,000

295,100

2.03%

265

California

1

5,000

260,300

1.92%

356

California

2

4,700

323,100

1.45%

425

California

3

2,600

286,600

0.91%

78

California

4

8,900

294,200

3.03%

221

California

5

6,900

326,800

2.11%

257

California

6

5,600

288,300

1.94%

53

California

7

10,600

313,200

3.38%

381

California

8

3,100

235,500

1.32%

336

California

9

4,200

275,300

1.53%

252

California

10

5,500

277,200

1.98%

281

California

11

6,000

324,200

1.85%

112

California

12

10,600

399,400

2.65%

68

California

13

10,700

340,200

3.15%

28

California

14

14,600

364,000

4.01%

6

California

15

26,900

336,400

8.00%

433

California

16

1,700

244,900

0.69%

1

California

17

59,500

346,100

17.19%

2

California

18

48,300

344,500

14.02%

3

California

19

38,600

324,000

11.91%

241

California

20

6,100

302,500

2.02%

436

California

21

200

243,800

0.08%

423

California

22

2,700

289,600

0.93%

432

California

23

2,000

274,100

0.73%

400

California

24

3,800

323,500

1.17%

151

California

25

7,500

302,700

2.48%

178

California

26

7,500

325,900

2.30%

132

California

27

8,500

332,200

2.56%

237

California

28

7,300

359,900

2.03%

156

California

29

7,400

303,700

2.44%

135

California

30

9,100

358,200

2.54%

240

California

31

5,900

292,200

2.02%

86

California

32

8,700

293,800

2.96%

187

California

33

8,200

364,200

2.25%

14

California

34

14,600

309,400

4.72%

31

California

35

11,300

284,800

3.97%

422

California

36

2,400

251,900

0.95%

108

California

37

9,000

335,600

2.68%

71

California

38

9,800

313,300

3.13%

37

California

39

12,600

332,000

3.80%

10

California

40

14,800

280,500

5.28%

214

California

41

5,800

271,900

2.13%

98

California

42

8,600

307,000

2.80%

128

California

43

7,800

302,800

2.58%

56

California

44

9,000

270,600

3.33%

16

California

45

16,100

354,400

4.54%

40

California

46

11,700

314,400

3.72%

154

California

47

8,000

327,600

2.44%

29

California

48

14,100

352,600

4.00%

30

California

49

11,900

299,700

3.97%

144

California

50

7,400

296,200

2.50%

301

California

51

4,500

258,600

1.74%

12

California

52

16,900

350,100

4.83%

184

California

53

7,800

342,700

2.28%

263

Colorado

1

7,400

384,400

1.93%

47

Colorado

2

13,400

384,600

3.48%

395

Colorado

3

4,000

331,400

1.21%

48

Colorado

4

11,800

344,100

3.43%

176

Colorado

5

7,300

315,900

2.31%

191

Colorado

6

8,200

369,600

2.22%

243

Colorado

7

7,300

362,500

2.01%

266

Connecticut

1

6,700

349,800

1.92%

264

Connecticut

2

6,700

348,600

1.92%

173

Connecticut

3

8,200

352,700

2.32%

172

Connecticut

4

8,000

343,000

2.33%

145

Connecticut

5

8,700

348,300

2.50%

363

Delaware

Statewide

6,000

420,400

1.43%

430

District of Columbia

Districtwide

2,300

310,600

0.74%

418

Florida

1

3,000

303,900

0.99%

397

Florida

2

3,600

301,500

1.19%

420

Florida

3

2,700

277,000

0.97%

364

Florida

4

4,700

329,900

1.42%

359

Florida

5

4,100

284,000

1.44%

338

Florida

6

4,300

283,200

1.52%

308

Florida

7

5,500

322,500

1.71%

100

Florida

8

7,900

283,400

2.79%

413

Florida

9

3,400

317,200

1.07%

385

Florida

10

4,300

331,500

1.30%

372

Florida

11

3,000

217,400

1.38%

201

Florida

12

6,200

283,200

2.19%

180

Florida

13

7,100

309,200

2.30%

332

Florida

14

5,000

320,700

1.56%

341

Florida

15

4,600

304,200

1.51%

320

Florida

16

4,500

276,100

1.63%

435

Florida

17

1,500

248,700

0.60%

367

Florida

18

4,000

284,000

1.41%

414

Florida

19

2,800

265,200

1.06%

348

Florida

20

4,500

302,100

1.49%

340

Florida

21

4,800

316,800

1.52%

207

Florida

22

7,200

332,000

2.17%

204

Florida

23

7,400

339,900

2.18%

362

Florida

24

4,200

293,400

1.43%

300

Florida

25

5,700

326,000

1.75%

383

Florida

26

4,400

335,600

1.31%

312

Florida

27

5,300

313,600

1.69%

403

Georgia

1

3,300

286,100

1.15%

267

Georgia

2

4,800

251,200

1.91%

89

Georgia

3

8,400

285,800

2.94%

269

Georgia

4

5,900

311,700

1.89%

279

Georgia

5

5,900

318,100

1.85%

129

Georgia

6

9,300

361,200

2.57%

51

Georgia

7

10,600

312,500

3.39%

345

Georgia

8

4,100

272,700

1.50%

81

Georgia

9

8,500

284,600

2.99%

228

Georgia

10

6,000

287,400

2.09%

167

Georgia

11

8,000

340,900

2.35%

258

Georgia

12

5,400

278,200

1.94%

298

Georgia

13

5,500

312,800

1.76%

7

Georgia

14

17,600

290,700

6.05%

394

Hawaii

1

4,000

330,100

1.21%

431

Hawaii

2

2,200

299,400

0.73%

83

Idaho

1

9,800

329,900

2.97%

197

Idaho

2

7,800

355,000

2.20%

328

Illinois

1

4,600

290,200

1.59%

259

Illinois

2

5,400

278,200

1.94%

229

Illinois

3

6,600

319,500

2.07%

97

Illinois

4

9,200

326,600

2.82%

175

Illinois

5

9,200

397,600

2.31%

13

Illinois

6

17,000

355,600

4.78%

318

Illinois

7

4,900

298,500

1.64%

41

Illinois

8

13,100

366,300

3.58%

110

Illinois

9

9,300

347,200

2.68%

52

Illinois

10

11,000

324,800

3.39%

66

Illinois

11

11,000

347,300

3.17%

331

Illinois

12

4,700

301,000

1.56%

380

Illinois

13

4,300

326,600

1.32%

67

Illinois

14

11,100

351,000

3.16%

276

Illinois

15

5,900

316,500

1.86%

205

Illinois

16

7,200

330,800

2.18%

169

Illinois

17

7,300

311,700

2.34%

262

Illinois

18

6,500

337,500

1.93%

226

Indiana

1

6,500

310,600

2.09%

62

Indiana

2

10,200

317,800

3.21%

43

Indiana

3

11,600

327,000

3.55%

168

Indiana

4

7,700

328,500

2.34%

235

Indiana

5

7,300

357,700

2.04%

96

Indiana

6

8,800

311,900

2.82%

171

Indiana

7

7,300

312,200

2.34%

60

Indiana

8

10,700

329,300

3.25%

185

Indiana

9

7,700

339,400

2.27%

138

Iowa

1

9,900

392,300

2.52%

183

Iowa

2

8,500

373,400

2.28%

335

Iowa

3

6,000

390,800

1.54%

360

Iowa

4

5,500

382,300

1.44%

405

Kansas

1

3,900

345,900

1.13%

323

Kansas

2

5,500

339,900

1.62%

181

Kansas

3

8,500

370,300

2.30%

404

Kansas

4

3,800

332,900

1.14%

174

Kentucky

1

6,600

284,800

2.32%

109

Kentucky

2

8,500

317,100

2.68%

114

Kentucky

3

8,800

333,300

2.64%

246

Kentucky

4

6,700

333,500

2.01%

357

Kentucky

5

3,400

234,300

1.45%

50

Kentucky

6

11,400

335,400

3.40%

409

Louisiana

1

3,900

354,000

1.10%

421

Louisiana

2

3,200

329,000

0.97%

411

Louisiana

3

3,600

328,100

1.10%

393

Louisiana

4

3,800

311,100

1.22%

419

Louisiana

5

2,800

283,900

0.99%

408

Louisiana

6

4,100

367,800

1.11%

287

Maine

1

6,200

340,400

1.82%

270

Maine

2

5,700

302,700

1.88%

333

Maryland

1

5,300

342,300

1.55%

384

Maryland

2

4,600

351,700

1.31%

371

Maryland

3

5,100

369,500

1.38%

354

Maryland

4

5,600

384,100

1.46%

390

Maryland

5

4,600

368,200

1.25%

288

Maryland

6

6,600

363,200

1.82%

375

Maryland

7

4,300

315,700

1.36%

311

Maryland

8

6,800

400,100

1.70%

297

Massachusetts

1

6,000

341,000

1.76%

25

Massachusetts

2

14,800

356,500

4.15%

9

Massachusetts

3

20,000

355,400

5.63%

39

Massachusetts

4

14,000

374,800

3.74%

55

Massachusetts

5

12,900

387,400

3.33%

127

Massachusetts

6

9,600

372,000

2.58%

334

Massachusetts

7

5,700

369,800

1.54%

202

Massachusetts

8

8,200

375,600

2.18%

188

Massachusetts

9

7,900

352,300

2.24%

322

Michigan

1

4,700

290,200

1.62%

74

Michigan

2

9,700

315,900

3.07%

121

Michigan

3

8,200

315,300

2.60%

216

Michigan

4

6,100

286,300

2.13%

282

Michigan

5

4,900

264,800

1.85%

150

Michigan

6

7,700

310,400

2.48%

247

Michigan

7

6,000

299,100

2.01%

195

Michigan

8

7,300

330,800

2.21%

193

Michigan

9

7,200

326,100

2.21%

124

Michigan

10

8,000

308,700

2.59%

149

Michigan

11

8,500

342,100

2.48%

327

Michigan

12

5,000

313,800

1.59%

277

Michigan

13

4,300

230,700

1.86%

286

Michigan

14

4,700

257,700

1.82%

15

Minnesota

1

16,400

348,200

4.71%

24

Minnesota

2

15,000

358,300

4.19%

21

Minnesota

3

15,000

353,800

4.24%

155

Minnesota

4

8,200

336,000

2.44%

113

Minnesota

5

9,300

352,000

2.64%

64

Minnesota

6

11,100

348,700

3.18%

209

Minnesota

7

7,100

328,700

2.16%

244

Minnesota

8

6,100

303,400

2.01%

42

Mississippi

1

10,900

305,600

3.57%

329

Mississippi

2

4,200

266,900

1.57%

306

Mississippi

3

5,200

303,900

1.71%

314

Mississippi

4

5,100

304,900

1.67%

352

Missouri

1

4,900

331,500

1.48%

198

Missouri

2

8,300

378,600

2.19%

309

Missouri

3

6,300

370,000

1.70%

353

Missouri

4

4,800

324,900

1.48%

303

Missouri

5

6,000

345,300

1.74%

321

Missouri

6

5,800

355,900

1.63%

208

Missouri

7

7,300

337,400

2.16%

222

Missouri

8

6,300

298,500

2.11%

426

Montana

Statewide

4,200

480,000

0.88%

307

Nebraska

1

5,500

321,700

1.71%

293

Nebraska

2

5,600

316,300

1.77%

416

Nebraska

3

3,100

305,600

1.01%

415

Nevada

1

3,000

284,700

1.05%

284

Nevada

2

5,700

309,400

1.84%

402

Nevada

3

3,900

336,500

1.16%

396

Nevada

4

3,300

274,300

1.20%

65

New Hampshire

1

11,200

352,600

3.18%

33

New Hampshire

2

12,800

332,200

3.85%

256

New Jersey

1

6,600

339,200

1.95%

368

New Jersey

2

4,500

324,400

1.39%

292

New Jersey

3

6,200

344,200

1.80%

239

New Jersey

4

6,600

326,400

2.02%

93

New Jersey

5

10,400

356,100

2.92%

157

New Jersey

6

8,600

353,600

2.43%

49

New Jersey

7

12,900

377,100

3.42%

120

New Jersey

8

9,700

371,000

2.61%

140

New Jersey

9

8,500

338,500

2.51%

274

New Jersey

10

5,800

310,700

1.87%

104

New Jersey

11

9,800

358,800

2.73%

242

New Jersey

12

7,100

352,400

2.01%

234

New Mexico

1

6,400

311,900

2.05%

429

New Mexico

2

2,100

273,100

0.77%

342

New Mexico

3

4,300

284,800

1.51%

141

New York

1

8,600

343,300

2.51%

139

New York

2

9,000

357,800

2.52%

273

New York

3

6,300

336,700

1.87%

374

New York

4

4,700

342,500

1.37%

315

New York

5

5,600

336,200

1.67%

305

New York

6

5,600

327,000

1.71%

159

New York

7

7,800

322,200

2.42%

346

New York

8

4,400

292,700

1.50%

366

New York

9

4,600

324,900

1.42%

347

New York

10

5,400

360,300

1.50%

351

New York

11

4,700

317,500

1.48%

290

New York

12

7,600

418,800

1.81%

350

New York

13

4,700

317,200

1.48%

337

New York

14

5,200

341,800

1.52%

358

New York

15

3,700

255,900

1.45%

326

New York

16

5,200

323,600

1.61%

249

New York

17

6,800

341,400

1.99%

18

New York

18

14,800

332,100

4.46%

84

New York

19

9,700

327,300

2.96%

296

New York

20

6,300

357,600

1.76%

272

New York

21

5,800

309,200

1.88%

131

New York

22

8,200

320,200

2.56%

182

New York

23

7,400

324,600

2.28%

166

New York

24

7,700

327,300

2.35%

73

New York

25

10,300

335,400

3.07%

294

New York

26

5,800

327,700

1.77%

199

New York

27

7,400

337,800

2.19%

186

North Carolina

1

6,600

291,800

2.26%

20

North Carolina

2

12,900

303,800

4.25%

398

North Carolina

3

3,600

305,600

1.18%

95

North Carolina

4

10,000

350,900

2.85%

44

North Carolina

5

11,500

324,500

3.54%

35

North Carolina

6

13,100

341,800

3.83%

324

North Carolina

7

5,100

315,400

1.62%

27

North Carolina

8

12,400

301,700

4.11%

111

North Carolina

9

9,900

371,400

2.67%

23

North Carolina

10

13,600

324,000

4.20%

88

North Carolina

11

8,700

295,400

2.95%

76

North Carolina

12

9,800

319,800

3.06%

32

North Carolina

13

13,600

349,900

3.89%

424

North Dakota

Statewide

3,400

370,800

0.92%

261

Ohio

1

6,400

332,300

1.93%

280

Ohio

2

6,000

323,600

1.85%

278

Ohio

3

6,200

333,000

1.86%

91

Ohio

4

9,300

317,900

2.93%

105

Ohio

5

9,100

334,200

2.72%

190

Ohio

6

6,500

292,300

2.22%

90

Ohio

7

9,600

326,800

2.94%

119

Ohio

8

8,600

328,800

2.62%

224

Ohio

9

6,600

315,000

2.10%

206

Ohio

10

6,800

312,800

2.17%

289

Ohio

11

5,000

275,200

1.82%

255

Ohio

12

7,000

359,500

1.95%

107

Ohio

13

8,600

320,400

2.68%

80

Ohio

14

10,500

349,700

3.00%

283

Ohio

15

6,200

336,400

1.84%

137

Ohio

16

9,000

355,600

2.53%

130

Oklahoma

1

9,300

361,900

2.57%

304

Oklahoma

2

5,000

290,300

1.72%

378

Oklahoma

3

4,400

329,900

1.33%

223

Oklahoma

4

7,400

350,900

2.11%

319

Oklahoma

5

5,700

348,800

1.63%

5

Oregon

1

31,600

377,200

8.38%

317

Oregon

2

5,200

314,200

1.65%

87

Oregon

3

11,300

383,300

2.95%

179

Oregon

4

7,100

309,000

2.30%

164

Oregon

5

7,700

326,700

2.36%

299

Pennsylvania

1

4,800

273,300

1.76%

379

Pennsylvania

2

3,600

273,100

1.32%

94

Pennsylvania

3

9,100

317,700

2.86%

163

Pennsylvania

4

8,100

342,900

2.36%

162

Pennsylvania

5

7,500

316,800

2.37%

123

Pennsylvania

6

9,400

362,300

2.59%

194

Pennsylvania

7

7,500

339,700

2.21%

122

Pennsylvania

8

9,300

357,800

2.60%

215

Pennsylvania

9

6,500

304,800

2.13%

146

Pennsylvania

10

7,800

312,500

2.50%

217

Pennsylvania

11

7,000

329,300

2.13%

142

Pennsylvania

12

8,300

331,900

2.50%

225

Pennsylvania

13

7,100

339,000

2.09%

254

Pennsylvania

14

6,300

323,200

1.95%

99

Pennsylvania

15

9,600

343,800

2.79%

161

Pennsylvania

16

7,800

327,700

2.38%

147

Pennsylvania

17

7,800

312,600

2.50%

148

Pennsylvania

18

8,600

345,000

2.49%

106

Rhode Island

1

6,800

250,900

2.71%

101

Rhode Island

2

7,200

260,300

2.77%

361

South Carolina

1

4,300

299,800

1.43%

230

South Carolina

2

6,300

305,600

2.06%

36

South Carolina

3

10,100

264,500

3.82%

63

South Carolina

4

9,600

301,000

3.19%

58

South Carolina

5

9,100

275,200

3.31%

253

South Carolina

6

5,000

253,500

1.97%

170

South Carolina

7

6,300

269,400

2.34%

339

South Dakota

1

6,300

415,600

1.52%

125

Tennessee

1

7,700

297,600

2.59%

285

Tennessee

2

6,000

327,200

1.83%

117

Tennessee

3

7,800

297,000

2.63%

92

Tennessee

4

9,200

314,500

2.93%

103

Tennessee

5

9,700

353,400

2.74%

158

Tennessee

6

7,400

304,500

2.43%

79

Tennessee

7

8,600

285,800

3.01%

177

Tennessee

8

6,900

299,200

2.31%

248

Tennessee

9

6,100

305,300

2.00%

313

Texas

1

5,000

297,700

1.68%

22

Texas

2

15,400

364,600

4.22%

8

Texas

3

21,100

371,200

5.68%

212

Texas

4

6,400

299,300

2.14%

238

Texas

5

6,100

300,800

2.03%

160

Texas

6

8,400

348,800

2.41%

72

Texas

7

11,600

376,300

3.08%

260

Texas

8

6,000

309,200

1.94%

196

Texas

9

7,200

326,400

2.21%

11

Texas

10

16,900

342,600

4.93%

410

Texas

11

3,400

308,800

1.10%

85

Texas

12

10,000

337,500

2.96%

389

Texas

13

3,900

309,000

1.26%

386

Texas

14

3,900

303,300

1.29%

399

Texas

15

3,300

280,900

1.17%

250

Texas

16

5,600

281,300

1.99%

26

Texas

17

13,600

329,300

4.13%

17

Texas

18

13,700

306,400

4.47%

407

Texas

19

3,500

310,700

1.13%

388

Texas

20

4,000

311,400

1.28%

192

Texas

21

8,000

361,200

2.21%

251

Texas

22

7,000

352,500

1.99%

391

Texas

23

3,600

289,700

1.24%

54

Texas

24

13,100

388,600

3.37%

34

Texas

25

11,600

302,200

3.84%

133

Texas

26

9,400

368,300

2.55%

330

Texas

27

4,800

305,600

1.57%

406

Texas

28

3,000

266,300

1.13%

219

Texas

29

6,200

292,900

2.12%

200

Texas

30

6,400

292,300

2.19%

4

Texas

31

34,400

323,000

10.65%

38

Texas

32

13,600

360,900

3.77%

46

Texas

33

9,900

283,900

3.49%

412

Texas

34

2,600

242,200

1.07%

213

Texas

35

6,800

318,200

2.14%

343

Texas

36

4,400

291,900

1.51%

220

Utah

1

6,600

312,400

2.11%

189

Utah

2

6,800

305,700

2.22%

136

Utah

3

7,900

311,200

2.54%

165

Utah

4

7,800

331,500

2.35%

116

Vermont

Statewide

8,600

327,300

2.63%

344

Virginia

1

5,300

352,400

1.50%

370

Virginia

2

4,700

339,800

1.38%

382

Virginia

3

4,200

320,100

1.31%

295

Virginia

4

5,800

327,900

1.77%

218

Virginia

5

6,700

316,100

2.12%

271

Virginia

6

6,400

339,900

1.88%

275

Virginia

7

6,800

364,600

1.87%

417

Virginia

8

4,200

423,700

0.99%

102

Virginia

9

8,200

298,400

2.75%

203

Virginia

10

8,200

376,400

2.18%

365

Virginia

11

5,700

400,900

1.42%

82

Washington

1

9,900

332,300

2.98%

387

Washington

2

4,100

318,900

1.29%

70

Washington

3

8,900

284,500

3.13%

427

Washington

4

2,300

284,500

0.81%

325

Washington

5

4,700

291,500

1.61%

392

Washington

6

3,400

275,500

1.23%

233

Washington

7

7,800

380,000

2.05%

369

Washington

8

4,400

318,000

1.38%

268

Washington

9

6,500

341,400

1.90%

231

Washington

10

6,000

291,300

2.06%

310

West Virginia

1

4,400

258,700

1.70%

377

West Virginia

2

3,600

266,900

1.35%

401

West Virginia

3

2,600

223,000

1.17%

75

Wisconsin

1

10,500

342,500

3.07%

210

Wisconsin

2

8,400

390,000

2.15%

77

Wisconsin

3

10,700

353,500

3.03%

143

Wisconsin

4

7,700

308,000

2.50%

57

Wisconsin

5

12,300

370,600

3.32%

69

Wisconsin

6

11,100

353,600

3.14%

134

Wisconsin

7

8,600

338,400

2.54%

118

Wisconsin

8

9,500

362,800

2.62%

434

Wyoming

Statewide

2,000

290,000

0.69%

Total*

3,360,600

140,400,900

2.39%

* Totals may vary slightly due to rounding.

Source: Authors’ analysis of U.S. Census Bureau 2013, U.S. International Trade Commission 2018, Bureau of Labor Statistics Employment Projections program 2017a and 2017b. For a more detailed explanation of data sources and computations, see the appendix.

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